Drone control via gestures using MediaPipe Hands

A guest post by Neurons Lab

Please note that the information, uses, and applications expressed in the below post are solely those of our guest author, Neurons Lab, and not necessarily those of Google.

How the idea emerged

With the advancement of technology, drones have become not only smaller, but also have more compute. There are many examples of iPhone-sized quadcopters in the consumer drone market and the computing power to do live tracking while recording 4K video. However, the most important element has not changed much – the controller. It is still bulky and not intuitive for beginners to use. There is a smartphone with on-display control as an option; however, the control principle is still the same. 

That is how the idea for this project emerged: a more personalised approach to control the drone using gestures. ML Engineer Nikita Kiselov (me) together with consultation from my colleagues at Neurons Lab undertook this project. 

Demonstration of drone flight control via gestures using MediaPipe Hands

Figure 1: [GIF] Demonstration of drone flight control via gestures using MediaPipe Hands

Why use gesture recognition?

Gestures are the most natural way for people to express information in a non-verbal way.  Gesture control is an entire topic in computer science that aims to interpret human gestures using algorithms. Users can simply control devices or interact without physically touching them. Nowadays, such types of control can be found from smart TV to surgery robots, and UAVs are not the exception.

Although gesture control for drones have not been widely explored lately, the approach has some advantages:

  • No additional equipment needed.
  • More human-friendly controls.
  • All you need is a camera that is already on all drones.

With all these features, such a control method has many applications.

Flying action camera. In extreme sports, drones are a trendy video recording tool. However, they tend to have a very cumbersome control panel. The ability to use basic gestures to control the drone (while in action)  without reaching for the remote control would make it easier to use the drone as a selfie camera. And the ability to customise gestures would completely cover all the necessary actions.

This type of control as an alternative would be helpful in an industrial environment like, for example, construction conditions when there may be several drone operators (gesture can be used as a stop-signal in case of losing primary source of control).

The Emergencies and Rescue Services could use this system for mini-drones indoors or in hard-to-reach places where one of the hands is busy. Together with the obstacle avoidance system, this would make the drone fully autonomous, but still manageable when needed without additional equipment.

Another area of application is FPV (first-person view) drones. Here the camera on the headset could be used instead of one on the drone to recognise gestures. Because hand movement can be impressively precise, this type of control, together with hand position in space, can simplify the FPV drone control principles for new users. 

However, all these applications need a reliable and fast (really fast) recognition system. Existing gesture recognition systems can be fundamentally divided into two main categories: first – where special physical devices are used, such as smart gloves or other on-body sensors; second – visual recognition using various types of cameras. Most of those solutions need additional hardware or rely on classical computer vision techniques. Hence, that is the fast solution, but it’s pretty hard to add custom gestures or even motion ones. The answer we found is MediaPipe Hands that was used for this project.

Overall project structure

To create the proof of concept for the stated idea, a Ryze Tello quadcopter was used as a UAV. This drone has an open Python SDK, which greatly simplified the development of the program. However, it also has technical limitations that do not allow it to run gesture recognition on the drone itself (yet). For this purpose a regular PC or Mac was used. The video stream from the drone and commands to the drone are transmitted via regular WiFi, so no additional equipment was needed. 

To make the program structure as plain as possible and add the opportunity for easily adding gestures, the program architecture is modular, with a control module and a gesture recognition module. 

Scheme that shows overall project structure and how videostream data from the drone is processed

Figure 2: Scheme that shows overall project structure and how videostream data from the drone is processed

The application is divided into two main parts: gesture recognition and drone controller. Those are independent instances that can be easily modified. For example, to add new gestures or change the movement speed of the drone.

Video stream is passed to the main program, which is a simple script with module initialisation, connections, and typical for the hardware while-true cycle. Frame for the videostream is passed to the gesture recognition module. After getting the ID of the recognised gesture, it is passed to the control module, where the command is sent to the UAV. Alternatively, the user can control a drone from the keyboard in a more classical manner.

So, you can see that the gesture recognition module is divided into keypoint detection and gesture classifier. Exactly the bunch of the MediaPipe key point detector along with the custom gesture classification model distinguishes this gesture recognition system from most others.

Gesture recognition with MediaPipe

Utilizing MediaPipe Hands is a winning strategy not only in terms of speed, but also in flexibility. MediaPipe already has a simple gesture recognition calculator that can be inserted into the pipeline. However, we needed a more powerful solution with the ability to quickly change the structure and behaviour of the recognizer. To do so and classify gestures, the custom neural network was created with 4 Fully-Connected layers and 1 Softmax layer for classification.

Figure 3: Scheme that shows the structure of classification neural network

Figure 3: Scheme that shows the structure of classification neural network

This simple structure gets a vector of 2D coordinates as an input and gives the ID of the classified gesture. 

Instead of using cumbersome segmentation models with a more algorithmic recognition process, a simple neural network can easily handle such tasks. Recognising gestures by keypoints, which is a simple vector with 21 points` coordinates, takes much less data and time. What is more critical, new gestures can be easily added because model retraining tasks take much less time than the algorithmic approach.

To train the classification model, dataset with keypoints` normalised coordinates and ID of a gesture was used. The numerical characteristic of the dataset was that:

  • 3 gestures with 300+ examples (basic gestures)
  • 5 gestures with 40 -150 examples 

All data is a vector of x, y coordinates that contain small tilt and different shapes of hand during data collection.

Figure 4: Confusion matrix and classification report for classification

Figure 4: Confusion matrix and classification report for classification

We can see from the classification report that the precision of the model on the test dataset (this is 30% of all data) demonstrated almost error-free for most classes, precision > 97% for any class. Due to the simple structure of the model, excellent accuracy can be obtained with a small number of examples for training each class. After conducting several experiments, it turned out that we just needed the dataset with less than 100 new examples for good recognition of new gestures. What is more important, we don’t need to retrain the model for each motion in different illumination because MediaPipe takes over all the detection work.

Figure 5: [GIF] Test that demonstrates how fast classification network can distinguish newly trained gestures using the information from MediaPipe hand detector

Figure 5: [GIF] Test that demonstrates how fast classification network can distinguish newly trained gestures using the information from MediaPipe hand detector

From gestures to movements

To control a drone, each gesture should represent a command for a drone. Well, the most excellent part about Tello is that it has a ready-made Python API to help us do that without explicitly controlling motors hardware. We just need to set each gesture ID to a command.

Figure 6: Command-gesture pairs representation

Figure 6: Command-gesture pairs representation

Each gesture sets the speed for one of the axes; that’s why the drone’s movement is smooth, without jitter. To remove unnecessary movements due to false detection, even with such a precise model, a special buffer was created, which is saving the last N gestures. This helps to remove glitches or inconsistent recognition.

The fundamental goal of this project is to demonstrate the superiority of the keypoint-based gesture recognition approach compared to classical methods. To demonstrate all the potential of this recognition model and its flexibility, there is an ability to create the dataset on the fly … on the drone`s flight! You can create your own combinations of gestures or rewrite an existing one without collecting massive datasets or manually setting a recognition algorithm. By pressing the button and ID key, the vector of detected points is instantly saved to the overall dataset. This new dataset can be used to retrain classification network to add new gestures for the detection. For now, there is a notebook that can be run on Google Colab or locally. Retraining the network-classifier takes about 1-2 minutes on a standard CPU instance. The new binary file of the model can be used instead of the old one. It is as simple as that. But for the future, there is a plan to do retraining right on the mobile device or even on the drone.

Figure 7: Notebook for model retraining in action

Figure 7: Notebook for model retraining in action

Summary 

This project is created to make a push in the area of the gesture-controlled drones. The novelty of the approach lies in the ability to add new gestures or change old ones quickly. This is made possible thanks to MediaPipe Hands. It works incredibly fast, reliably, and ready out of the box, making gesture recognition very fast and flexible to changes. Our Neuron Lab`s team is excited about the demonstrated results and going to try other incredible solutions that MediaPipe provides. 

We will also keep track of MediaPipe updates, especially about adding more flexibility in creating custom calculators for our own models and reducing barriers to entry when creating them. Since at the moment our classifier model is outside the graph, such improvements would make it possible to quickly implement a custom calculator with our model into reality.

Another highly anticipated feature is Flutter support (especially for iOS). In the original plans, the inference and visualisation were supposed to be on a smartphone with NPUGPU utilisation, but at the moment support quality does not satisfy our requests. Flutter is a very powerful tool for rapid prototyping and concept checking. It allows us to throw and test an idea cross-platform without involving a dedicated mobile developer, so such support is highly demanded. 

Nevertheless, the development of this demo project continues with available functionality, and there are already several plans for the future. Like using the MediaPipe Holistic for face recognition and subsequent authorisation. The drone will be able to authorise the operator and give permission for gesture control. It also opens the way to personalisation. Since the classifier network is straightforward, each user will be able to customise gestures for themselves (simply by using another version of the classifier model). Depending on the authorised user, one or another saved model will be applied. Also in the plans to add the usage of Z-axis. For example, tilt the palm of your hand to control the speed of movement or height more precisely. We encourage developers to innovate responsibly in this area, and to consider responsible AI practices such as testing for unfair biases and designing with safety and privacy in mind.

We highly believe that this project will motivate even small teams to do projects in the field of ML computer vision for the UAV, and MediaPipe will help to cope with the limitations and difficulties on their way (such as scalability, cross-platform support and GPU inference).

If you want to contribute, have ideas or comments about this project, please reach out to [email protected], or visit the GitHub page of the project.

This blog post is curated by Igor Kibalchich, ML Research Product Manager at Google AI.

Machine Learning GDEs: Q2 ‘21 highlights and achievements

Posted by HyeJung Lee, MJ You, ML Ecosystem Community Managers

Google Developers Experts (GDE) is a community of passionate developers who love to share their knowledge with others. Many of them specialize in Machine Learning (ML).

Here are some highlights showcasing the ML GDEs achievements from last quarter, which contributed to the global ML ecosystem. If you are interested in becoming an ML GDE, please scroll down to see how you can apply!

ML Developers meetup @Google I/O

ML Developer meetup at Google I/O

At I/O this year, we held two ML Developers Meetups (America/APAC and EMEA/APAC). Merve Noyan/Yusuf Sarıgöz (Turkey), Sayak Paul/Bhavesh Bhatt (India), Leigh Johnson/Margaret Maynard-Reid (USA), David Cardozo (Columbia), Vinicius Caridá/Arnaldo Gualberto (Brazil) shared their experiences in developing ML products with TensorFlow, Cloud AI or JAX and also introduced projects they are currently working on.

I/O Extended 2021

Chart showing what's included in Vertex AI

After I/O, many ML GDEs posted recap summaries of the I/O on their blogs. Chansung Park (Korea) outlined the ML keynote summary, while US-based Victor Dibia wrapped up the Top 10 Machine Learning and Design Insights from Google IO 2021.

Vertex AI was the topic of conversation at the event. Minori Matsuda from Japan wrote a Japanese article titled “Introduction of powerful Vertex AI AutoML Forecasting.” Similarly, Piero Esposito (Brazil) posted an article titled “Serverless Machine Learning Pipelines with Vertex AI: An Introduction,” including a tutorial on fully customized code. India-based Sayak Paul co-authored a blog post discussing key pieces in Vertex AI right after the Vertex AI announcement showing how to run a TensorFlow training job using Vertex AI.

Communities such as Google Developers Groups (GDG) and TensorFlow User Groups (TFUG) held extended events where speakers further discussed different ML topics from I/O, including China-based Song Lin’s presentation on TensorFlow highlights and Applications experiences from I/O which had 24,000 online attendees. Chansung Park (Korea) also gave a presentation on what Vertex AI is and what you can do with Vertex AI.

Cloud AI

Cloud AI

Leigh Johnson (USA) wrote an article titled Soft-launching an AI/ML Product as a Solo Founder, covering GCP AutoML Vision, GCP IoT Core, TensorFlow Model Garden, and TensorFlow.js. The article details the journey of a solo founder developing an ML product for detecting printing failure for 3D printers (more on this story is coming up soon, so stay tuned!)

Demo and code examples from Victor Dibia (USA)’s New York Taxi project, Minori Matsuda (Japan)’s article on AutoML and AI Platform notebook, Srivatsan Srinivasan (USA)’s video tutorials, Sayak Paul (India)’s Distributed Training in TensorFlow with AI Platform & Docker and Chansung Park (Korea)’s curated personal newsletter were all published together on Cloud blog.

Aqsa Kausar (Pakistan) gave a talk about Explainable AI in Google Cloud at the International Women’s Day Philippines event. She explained why it is important and where and how it is applied in ML workflows.

Learn agenda

Finally, ML Lab by Robert John from Nigeria, introduces the ML landscape on GCP covering from BigQueryML through AutoML to TensorFlow and AI Platform.

TensorFlow

Image of TensorFlow 2 and Learning TensorFlow JS books

Eliyar Eziz (China) published a book “TensorFlow 2 with real-life use cases”. Gant Laborde from the US authored book “Learning TensorFlow.js” which is published by O’Reilly and wrote an article “No Data No Problem – TensorFlow.js Transfer Learning” about seeking out new datasets to boldly train where no models have trained before. He also published “A Riddikulus Dataset” which talks about creating the Harry Potter dataset.

Iterated dilated convolutional neural networks for word segmentation

Hong Kong-based Guan Wang published a research paper, “Iterated Dilated Convolutional Neural Networks for Word Segmentation,” covering state-of-the-art performance improvement, which is implemented on TensorFlow by Keras.

Elyes Manai from Tunisia wrote an article “Become a Tensorflow Certified Developer ” – a guide to TensorFlow Certificate and tips.

BERT model

Greece-based George Soloupis wrote a tutorial “Fine-tune a BERT model with the use of Colab TPU” on how to finetune a BERT model that was trained specifically on greek language to perform the downstream task of text classification, using Colab’s TPU (v2–8).

JAX

India-based Aakash Nain has published the TF-JAX tutorial series (Part1, Part2, Part3, Part 4), aiming to teach everyone the building blocks of TensorFlow and JAX frameworks.

TensorFlow with Jax thumbnail

Online Meetup TensorFlow and JAX by Tzer-jen Wei from Taiwan covered JAX intro and use cases. It also touched upon different ways of writing TensorFlow models and training loops.

Neural Networks, with a practical example written in JAX

YouTube video Neural Networks, with a practical example written in JAX, probably the first JAX techtalk in Portuguese by João Guilherme Madeia Araújo (Brazil).

Keras

Keras logo

A lot of Keras examples were contributed by Sayak Paul from India and listed below are some of these examples.

Kaggle

Kaggle character distribution chart

Notebook “Simple Bayesian Ridge with Sentence Embeddings” by Ertuğrul Demir (Turkey) about a natural language processing task using BERT finetuning followed by simple linear regression on top of sentence embeddings generated by transformers.

TensorFlow logo screenshot from Learning machine learning and tensorflow with Kaggle competition video

Youhan Lee from Korea gave a talk about “Learning machine learning and TensorFlow with Kaggle competition”. He explained how to use the Kaggle platform for learning ML.

Research

Advances in machine learning and deep learning research are changing our technology, and many ML GDEs are interested and contributing.

Learning Neurl Compositional Neural Programs for Continuous Control

Karim Beguir (UK) co-authored a paper with the DeepMind team covering a novel compositional approach using Deep Reinforcement Learning to solve robotics manipulation tasks. The paper was accepted in the NeurIPS workshop.

Finally, Sayak Paul from India, together with Pin-Yu Chen, published a research paper, “Vision Transformers are Robust Learners,” covering the robustness of the Vision Transformer (ViT) against common corruptions and perturbations, distribution shifts, and natural adversarial examples.

If you want to know more about the Google Experts community and their global open-source ML contributions, please check the GDE Program website, visit the GDE Directory and connect with GDEs on Twitter and LinkedIn. You can also meet them virtually on the ML GDE’s YouTube Channel!

Developer updates from Coral

Posted by The Coral Team

We’re always excited to share updates to our Coral platform for building edge ML applications. In this post, we have some interesting demos, interfaces, and tutorials to share, and we’ll start by pointing you to an important software update for the Coral Dev Board.

Important update for the Dev Board / SoM

If you have a Coral Dev Board or Coral SoM, please install our latest Mendel update as soon as possible to receive a critical fix to part of the SoC power configuration. To get it, just log onto your board and install the update as follows:

Dev Board / Som

This will install a patch from NXP for the Dev Board / SoM’s SoC, without which it’s possible the SoC will overstress and the lifetime of the device could be reduced. If you recently flashed your board with the latest system image, you might already have this fix (we also updated the flashable image today), but it never hurts to fetch all updates, as shown above.

Note: This update does not apply to the Dev Board Mini.

Manufacturing demo

We recently published the Coral Manufacturing Demo, which demonstrates how to use a single Coral Edge TPU to simultaneously accomplish two common manufacturing use-cases: worker safety and visual inspection.

The demo is designed for two specific videos and tasks (worker keepout detection and apple quality grading) but it is designed to be easily customized with different inputs and tasks. The demo, written in C++, requires OpenGL and is primarily targeted at x86 systems which are prevalent in manufacturing gateways – although ARM Cortex-A systems, like the Coral Dev Board, are also supported.

demo image

Web Coral

We’ve been working hard to make ML acceleration with the Coral Edge TPU available for most popular systems. So we’re proud to announce support for WebUSB, allowing you to use the Coral USB Accelerator directly from Chrome. To get started, check out our WebCoral demo, which builds a webpage where you can select a model and run an inference accelerated by the Edge TPU.

 Edge TPU

New models repository

We recently released a new models repository that makes it easier to explore the various trained models available for the Coral platform, including image classification, object detection, semantic segmentation, pose estimation, and speech recognition. Each family page lists the various models, including details about training dataset, input size, latency, accuracy, model size, and other parameters, making it easier to select the best fit for the application at hand. Lastly, each family page includes links to training scripts and example code to help you get started. Or for an overview of all our models, you can see them all on one page.

Models, trained TensorFlow models for the Edge TPU

Transfer learning tutorials

Even with our collection of pre-trained models, it can sometimes be tricky to create a task-specific model that’s compatible with our Edge TPU accelerator. To make this easier, we’ve released some new Google Colab tutorials that allow you to perform transfer learning for object detection, using MobileDet and EfficientDet-Lite models. You can find these and other Colabs in our GitHub Tutorials repo.

We are excited to share all that Coral has to offer as we continue to evolve our platform. Keep an eye out for more software and platform related news coming this summer. To discover more about our edge ML platform, please visit Coral.ai and share your feedback at [email protected].

Machine Learning GDEs: Q1 2021 highlights, projects and achievements

Posted by HyeJung Lee and MJ You, Google ML Ecosystem Community Managers. Reviewed by Soonson Kwon, Developer Relations Program Manager.

Google Developers Experts is a community of passionate developers who love to share their knowledge with others. Many of them specialize in Machine Learning (ML). Despite many unexpected changes over the last months and reduced opportunities for various in person activities during the ongoing pandemic, their enthusiasm did not stop.

Here are some highlights of the ML GDE’s hard work during the Q1 2021 which contributed to the global ML ecosystem.

ML GDE YouTube channel

ML GDE YouTube page

With the initiative and lead of US-based GDE Margaret Maynard-Reid, we launched the ML GDEs YouTube channel. It is a great way for GDEs to reach global audiences, collaborate as a community, create unique content and promote each other’s work. It will contain all kinds of ML related topics: talks on technical topics, tutorials, interviews with another (ML) GDE, a Googler or anyone in the ML community etc. Many videos have already been uploaded, including: ML GDE’s intro from all over the world, tips for TensorFlow & GCP Certification and how to use Google Cloud Platform etc. Subscribe to the channel now!!

TensorFlow Everywhere

TensorFlow Everywhere logo

17 ML GDEs presented at TensorFlow Everywhere (a global community-led event series for TensorFlow and Machine Learning enthusiasts and developers around the world) hosted by local TensorFlow user groups. You can watch the recorded sessions in the TensorFlow Everywhere playlist on the ML GDE Youtube channel. Most of the sessions cover new features in Tensorflow.

International Women’s Day

Many ML GDEs participated in activities to celebrate International Women’s Day (March 8th). GDE Ruqiya Bin Safi (based in Saudi Arabia) cooperated with WTM Saudi Arabia to organize “Socialthon” – social development hackathons and gave a talk “Successful Experiences in Social Development“, which reached 77K viervers live and hit 10K replays. India-based GDE Charmi Chokshi participated in GirlScript’s International Women’s Day event and gave a talk: “Women In Tech and How we can help the underrepresented in the challenging world”. If you’re looking for more inspiring materials, check out the “Women in AI” playlist on our ML GDE YouTube channel!

Mentoring

ML GDEs are also very active in mentoring community developers, students in the Google Developer Student Clubs and startups in the Google for Startups Accelerator program. Among many, GDE Arnaldo Gualberto (Brazil) conducted mentorship sessions for startups in the Google Fast Track program, discussing how to solve challanges using Machine Learning/Deep Learning with TensorFlow.

TensorFlow

Practical Adversarial Robustness in Deep Learning: Problems and Solutions
ML using TF cookbook and ML for Dummies book

Meanwhile in Europe, GDEs Alexia Audevart (based in France) and Luca Massaron (based in Italy) released “Machine Learning using TensorFlow Cookbook”. It provides simple and effective ideas to successfully use TensorFlow 2.x in computer vision, NLP and tabular data projects. Additionally, Luca published the second edition of the Machine Learning For Dummies book, first published in 2015. Her latest edition is enhanced with product updates and the principal is a larger share of pages devoted to discussion of Deep Learning and TensorFlow / Keras usage.

YouTube video screenshot

On top of her women-in-tech related activities, Ruqiya Bin Safi is also running a “Welcome to Deep Learning Course and Orientation” monthly workshop throughout 2021. The course aims to help participants gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.

TensorFlow Project showcase

Nepal-based GDE Kshitiz Rimal gave a talk “TensorFlow Project Showcase: Cash Recognition for Visually Impaired” on his project which uses TensorFlow, Google Cloud AutoML and edge computing technologies to create a solution for the visually impaired community in Nepal.

Screenshot of TF Everywhere NA talk

On the other side of the world, in Canada, GDE Tanmay Bakshi presented a talk “Machine Learning-powered Pipelines to Augment Human Specialists” during TensorFlow Everywhere NA. It covered the world of NLP through Deep Learning, how it’s historically been done, the Transformer revolution, and how using the TensorFlow & Keras to implement use cases ranging from small-scale name generation to large-scale Amazon review quality ranking.

Google Cloud Platform

Google Cloud Platform YouTube playlist screenshot

We have been equally busy on the GCP side as well. In the US, GDE Srivatsan Srinivasan created a series of videos called “Artificial Intelligence on Google Cloud Platform”, with one of the episodes, “Google Cloud Products and Professional Machine Learning Engineer Certification Deep Dive“, getting over 3,000 views.

ML Analysis Pipeline

Korean GDE Chansung Park contributed to TensorFlow User Group Korea with his “Machine Learning Pipeline (CI/CD for ML Products in GCP)” analysis, focused on about machine learning pipeline in Google Cloud Platform.

Analytics dashboard

Last but not least, GDE Gad Benram based in Israel wrote an article on “Seven Tips for Forecasting Cloud Costs”, where he explains how to build and deploy ML models for time series forecasting with Google Cloud Run. It is linked with his solution of building a cloud-spend control system that helps users more-easily analyze their cloud costs.

If you want to know more about the Google Experts community and all their global open-source ML contributions, visit the GDE Directory and connect with GDEs on Twitter and LinkedIn. You can also meet them virtually on the ML GDE’s YouTube Channel!

ML Kit is now in GA & Introducing Selfie Segmentation


Posted by Kenny Sulaimon, Product Manager, ML Kit Chengji Yan, Suril Shah, Buck Bourdon, Software Engineers, ML Kit, Shiyu Hu, Technical Lead, ML Kit Dong Chen, Technical Lead, ML Kit

At the end of 2020, we introduced the Entity Extraction API to our ML Kit SDK, making it even easier to detect and perform actions on text within mobile apps. Since then, we’ve been hard at work updating our existing APIs with new functionality and also fine tuning Selfie Segmentation with the help of our partners in the ML Kit early access program.

Today we are excited to officially add Selfie Segmentation to the ML Kit lineup, introduce a few enhancements we’ve made to our popular Pose Detection API and announce that ML Kit has graduated to general availability!

Natural language graphic

General Availability

ML Kit image

(ML Kit is now in General Availability)

We launched ML Kit back in 2018 in order to make it easy for developers on Android and iOS to use machine learning within their apps. Over the last two years we have rapidly expanded our set of APIs to help with both vision and natural language processing based use cases.

Thanks to the overwhelmingly positive response, developer feedback and a ton of adoption across both Android and iOS, we are ready to drop the beta label and officially announce the general availability of ML Kit’s APIs. All of our APIs (except Selfie Segmentation, Pose Detection, and Entity Extraction) are now in general availability!

Selfie Segmentation

Selfie Segmentation photo

(Example of ML Kit Selfie Segmentation)

With the increased usage of selfie cameras and webcams in today’s world, being able to quickly and easily add effects to camera experiences has become a necessity for many app developers.

ML Kit’s Selfie Segmentation API allows developers to easily separate the background from users within a scene and focus on what matters. Adding cool effects to selfies or inserting your users into interesting background environments has never been easier. The model works on live and still images, and both half and full body subjects.

Under The Hood

Under the hood graph

(Diagram of Selfie Segmentation API)

The Selfie Segmentation API takes an input image and produces an output mask. Each pixel of the mask is assigned a float number that has a range between [0.0, 1.0]. The closer the number is to 1.0, the higher the confidence that the pixel represents a person, and vice versa.

The API works with static images and live video use cases. During live video (stream_mode), the API will leverage output from previous frames to return smoother segmentation results.

We’ve also implemented a “RAW_SIZE_MASK” option to give developers more options for mask output. By default, the mask produced will be the same size as the input image. If the RAW_SIZE_MASK option is enabled, then the mask will be the size of the model output (256×256). This option makes it easier to apply customized rescaling logic or reduces latency if rescaling to the input image size is not needed for your use case.

Pose Detection Update

Example of Pose Detection API

(Example of updated Pose Detection API; colors represent the Z value)

Last month, we updated our state-of-the-art Pose Detection API with a new model and new features. A quick summary of the enhancements is listed below:

  • More poses added The API now recognizes more poses, targeting fitness and yoga use cases, especially when a user is directly facing the camera.
  • 50% size reduction The base and accurate pose models are now significantly smaller. This change does not impact the quality of the models.
  • Z Coordinate for depth analysis The API now outputs a depth coordinate Z to help determine whether parts of the user’s body are in front or behind the user’s hips.

Z Coordinate

The Z Coordinate is an experimental feature that is calculated for every point (excluding the face). The estimate is provided using synthetic data, obtained via the GHUM model (articulated 3D human shape model).

It is measured in “image pixels” like the X and Y coordinates. The Z axis is perpendicular to the camera and passes between a subject’s hips. The origin of the Z axis is approximately the center point between the hips (left/right and front/back relative to the camera). Negative Z values are towards the camera; positive values are away from it. The Z coordinate does not have an upper or lower bound.

For more information on the Pose Detection changes, please see our API documentation.

Pose Classification

After the release of Pose Detection, we’ve received quite a bit of requests from developers to help with classifying specific poses within their apps. To help tackle this problem, we partnered with the MediaPipe team to release a pose classification tutorial and Google Colab. In the classification tutorial, we demonstrate how to build and run a custom pose classifier within the ML Kit Android sample app and also demo a simple push-up and squat rep counter using the classifier.

Example of Pose classification and repetition counting with MLKit Pose

(Example of Pose classification and repetition counting with MLKit Pose)

For a deep dive into building your own pose classifier with different camera angles, environment conditions, body shapes etc, please see the pose classification tutorial.

For more general classification tips, please see our Pose Classification Options page on the ML Kit website.

Beyond General Availability

It has been an exciting two years getting ML Kit to general availability and we couldn’t have gotten here without your help and feedback. As we continue to introduce new APIs such as Selfie Segmentation and Pose Detection, your feedback is more important than ever. Please continue to share your enhancement requests and questions with our development team or reach out through our community channels. Let’s build a smarter future together.

Announcing the Newest Addition to MLKit: Entity Extraction


Posted by Kenny Sulaimon, Product Manager, ML Kit, Tory Voight, Product Manager, ML Kit, Daniel Furlong, Lei Yu, Software Engineers, ML Kit, Dong Chen, Technical Lead, MLKit
ML Kit logo

Six months ago, we introduced the standalone version of the ML Kit SDK, making it even easier to integrate on-device machine learning into mobile apps. Since then, we’ve launched the Digital Ink Recognition and Pose Detection APIs, and also introduced the ML Kit early access program. Today we are excited to add Entity Extraction to the official ML Kit lineup and also debut a new API for our early access program, Selfie Segmentation!

Overview of ML Kit's Entity Extraction API

With ML Kit’s Entity Extraction API, you can now improve the user experience inside your app by understanding text and performing specific actions on it.

The Entity Extraction API allows you to detect and locate entities from raw text, and take action based on those entities. The API works on static text and also in real-time while a user is typing. It supports 11 different entities and 15 different languages (with more coming in the future) to allow developers to make any text interaction a richer experience for the user.

Supported Entities

  • Address (350 third street, cambridge)
  • Date-Time* (12/12/2020, tomorrow at 3pm) (let’s meet tomorrow at 6pm)
  • Email ([email protected])
  • Flight Number* (LX37)
  • IBAN* (CH52 0483 0000 0000 0000 9)
  • ISBN* (978-1101904190)
  • Money (including currency)* ($12, 25USD)
  • Payment Card* (4111 1111 1111 1111)
  • Phone Number ((555) 225-3556, 12345)
  • Tracking Number* (1Z204E380338943508)
  • URL (www.google.com, https://en.wikipedia.org/wiki/Platypus, seznam.cz)

Example Results

Table of Input text and Detected entities

Real World Applications

2 phones showing TamTam using Entity Extraction API

(Images courtesy of TamTam)

Our early access partner, TamTam, has been using the Entity Extraction API to provide helpful suggestions to their users during their chat conversations. This feature allows users to quickly perform actions based on the context of their conversations.

While integrating this API, Iurii Dorofeev, Head of TamTam Android Development, mentioned, “We appreciated the ease of integration of the ML Kit … and it works offline. Clustering the content of messages right on the device allowed us to save resources. ML Kit capabilities will help us develop other features for TamTam messenger in the future.”

Check out their messaging app on the Google Play and App Store today.

Under The Hood

Diagram of underlying Text Classifier API

(Diagram of underlying Text Classifier API)

ML Kit’s Entity Extraction API builds upon the technology powering the Smart Linkify feature in Android 10+ to deliver an easy-to-use and streamlined experience for developers. For an in-depth review of the Text Classifier API, please see our blog post here.

The neural network annotators/models in the Entity Extraction API work as follows: A given input text is first split into words (based on space separation), then all possible word subsequences of certain maximum length (15 words in the example above) are generated, and for each candidate the scoring neural net assigns a value (between 0 and 1) based on whether it represents a valid entity.

Next, the generated entities that overlap are removed, favoring the ones with a higher score over the conflicting ones with a lower score. Then a second neural network is used to classify the type of the entity as a phone number, an address, or in some cases, a non-entity.

The neural network models in the Entity Extraction API are used in combination with other types of models (e.g. rule-based) to identify additional entities in text, such as: flight numbers, currencies and other examples listed above. Therefore, if multiple entities are detected for one text input, the Entity Extraction API can return several overlapping results.

Lastly, ML Kit will automatically download the required language-specific models to the device dynamically. You can also explicitly manage models you want available on the device by using ML Kit’s model management API. This can be useful if you want to download models ahead of time for your users. The API also allows you to delete models that are no longer required.

New APIs Coming Soon

Selfie Segmentation

With the increased usage of selfie cameras and webcams in today’s world, being able to quickly and easily add effects to camera experiences has become a necessity for many app developers today.

ML Kit’s Selfie Segmentation API allows developers to easily separate the background from a scene and focus on what matters. Adding cool effects to selfies or inserting your users into interesting background environments has never been easier. This API produces great results with low latency on both Android and iOS devices.

Example of ML Kit Selfie Segmentation

(Example of ML Kit Selfie Segmentation)

Key capabilities:

  • Easily allows developers to replace or blur a user’s background
  • Works well with single or multiple people
  • Cross-platform support (iOS and Android)
  • Runs real-time on most modern phones

To join our early access program and request access to ML Kit’s Selfie Segmentation API, please fill out this form.

Aligning thousands of Billie Eilish covers in an infinite music video experiment


Posted by Google Creative Lab

Billie Eilish gif

“Bad Guy” by Billie Eilish is one of the most-covered songs on YouTube, inspiring thousands of fans to upload their own versions. To celebrate all these covers, YouTube and Google Creative Lab built an AI experiment to combine all of them seamlessly in the world’s first infinite music video: Infinite Bad Guy. The experience aligns every cover to the same beat, no matter its genre, language, or instrumentation.

Finding all the covers

How do you find “Bad Guy” covers amidst all the billions of videos on YouTube? Just searching for “Bad Guy” would result in false positives, like videos of Billie being interviewed about the song, or miss covers that didn’t use the song name in their titles. YouTube’s ContentID system allows us to find videos that match the musical composition “Bad Guy” and also allows us to narrow our search to videos that appear to be performances or creative interpretations of the song. That way, we can also avoid videos where “Bad Guy” was just background music. We continue to run this search daily, collecting an ever-expanding list of potential covers to use in the experience.

Finding all the covers

Aligning all the covers to the same beat

A key part of the experience is being able to jump from cover to cover seamlessly. But fan covers of “Bad Guy” vary widely. Some might be similar to the original, like a dance video set to Billie’s track. Some might vary more in tempo and instrumentation, like a heavy metal cover. And others might diverge greatly from the original, like a clarinet version with no lyrics. How can you get all these covers on the same beat? After trying several approaches like dynamic time warping and chord recognition, we’ve found the most success with a recurrent neural network trained to recognize sections and beats of “Bad Guy.” We collaborated with our friends at IYOYO on cover alignment and they have a great writeup about the process.

Aligning all the covers to the same beat

Building the experience

Finding and aligning the covers is a fascinating research problem, but the crucial final step is making them explorable to everyone. We’ve tried to make it intuitive and fun to navigate all the infinite combinations, while keeping latency low so the song never drops a beat.

The experience centers around three YouTube players, a number we settled on after a lot of experimentation. Initially we thought more players would be more interesting, but the experience got chaotic and slow. Around the players we’ve added discoverable features like the hashtag drawer and stats page. Video game interfaces have been a big inspiration for us, as they combine multiple interactions in a single dashboard. We’ve also added an autoplay mode for users who want to just sit back and be taken through an ever-changing mix of covers.

We’re excited about how Infinite Bad Guy showcases the incredibly diverse talent of YouTube and the potential machine learning can have for music and creativity. Give it a try and see what beautiful, strange, and brilliant covers you can find.

Irem from Turkey shares her groundbreaking work in TensorFlow and advice for the community


Posted by Jennifer Kohl, Global Program Manager, Google Developer Groups

Irem presenting at a Google Developer Group event

We recently caught up with Irem Komurcu, a TensorFlow developer and researcher at Istanbul Technical University in Turkey. Irem has been a long-serving member of Google Developer Groups (GDG) Düzce and also serves as a Women Techmakers (WTM) ambassador. Her work with TensorFlow has received several accolades, including being named a Hamdi Ulukaya Girişimi fellow. As one one of twenty-four young entrepreneurs selected, she was flown to New York City last year to learn more about business and receive professional development.

With all this experience to share, we wanted you to hear how she approaches pursuing a career in tech, hones her TensorFlow skills with the GDG community, and thinks about how upcoming programmers can best position themselves for success. Check out the full interview below for more.

What inspired you to pursue a career in technology?

I first became interested in tech when I was in high school and went on to study computer engineering. At university, I had an eye-opening experience when I traveled from Turkey to the Google Developer Day event in India. It was here where I observed various code languages, products, and projects that were new to me.

In particular, I saw TensorFlow in action for the first time. Watching the powerful machine learning tool truly sparked my interest in deep learning and project development.

Can you describe your work with TensorFlow and Machine Learning?

I have studied many different aspects of Tensorflow and ML. My first work was on voice recognition and deep learning. However, I am now working as a computer vision researcher conducting various segmentation, object detection, and classification processes with Tensorflow. In my free time, I write various articles about best practices and strategies to leverage TensorFlow in ML.

What has been a useful learning resource you have used in your career?

I kicked off my studies on deep learning on tensorflow.org. It’s a basic first step, but a powerful one. There were so many blogs, codes, examples, and tutorials for me to dive into. Both the Google Developer Group and TensorFlow communities also offered chances to bounce questions and ideas off other developers as I learned.

Between these technical resources and the person-to-person support, I was lucky to start working with the GDG community while also taking the first steps of my career. There were so many opportunities to meet people and grow all around.

What is your favorite part of the Google Developer Group community?

I love being in a large community with technology-oriented people. GDG is a network of professionals who support each other, and that enables people to develop. I am continuously sharing my knowledge with other programmers as they simultaneously mentor me. The chance for us to collaborate together is truly fulfilling.

What is unique about being a developer in your country/region?

The number of women supported in science, technology, engineering, and mathematics (STEM) is low in Turkey. To address this, I partner with Women Techmakers (WTM) to give educational talks on TensorFlow and machine learning to women who want to learn how to code in my country. So many women are interested in ML, but just need a friendly, familiar face to help them get started. With WTM, I’ve already given over 30 talks to women in STEM.

What advice would you give to someone who is trying to grow their career as a developer?

Keep researching new things. Read everything you can get your eyes on. Technology has been developing rapidly, and it is necessary to make sure your mind can keep up with the pace. That’s why I recommend communities like GDG that help make sure you’re up to date on the newest trends and learnings.

Want to work with other developers like Irem? Then find the right Google Developer Developer Group for you, here.

Passionate former DSC lead Irene inspires others to learn Google technologies with her new podcast and more


Posted by Erica Hanson, Global Program Manager, Google Developer Student Clubs

(Irene (left) and her DSC team from the Polytechnic University of Cartagena (photo prior to COVID-19)

Irene Ruiz Pozo is a former Google Developer Student Club (DSC) Lead at the Polytechnic University of Cartagena in Murcia, Spain. As one of the founding members, Irene has seen the club grow from just a few student developers at her university to hosting multiple learning events across Spain. Recently, we spoke with Irene to understand more about the unique ways in which her team helped local university students learn more about Google technologies.

Real world ML and AR learning opportunities

Irene mentioned two fascinating projects that she had the chance to work on through her DSC at the Polytechnic University of Cartagena. The first was a learning lab that helped students understand how to use 360º cameras and 3D scanners for machine learning.

(A DSC member giving a demo of a 360º camera to students at the National Museum of Underwater Archeology in Cartagena)

The second was a partnership with the National Museum of Underwater Archeology, where Irene and her team created an augmented reality game that let students explore a digital rendition of the museum’s exhibitions.

(An image from the augmented reality game created for the National Museum of Underwater Archeology)

In the above AR experience created by Irene’s team, users can create their own character and move throughout the museum and explore different virtual renditions of exhibits in a video game-like setting.

Hash Code competition and experiencing the Google work culture

One particularly memorable experience for Irene and her DSC was participating in Google’s annual programming competition, Hash Code. As Irene explained, the event allowed developers to share their skills and connect in small teams of two to four programmers. They would then come together to tackle engineering problems like how to best design the layout of a Google data center, create the perfect video streaming experience on YouTube, or establish the best practices for compiling code at Google scale.

(Students working on the Hash Code competition (photo taken prior to COVID-19)

To Irene, the experience felt like a live look at being a software engineer at Google. The event taught her and her DSC team that while programming skills are important, communication and collaboration skills are what really help solve problems. For Irene, the experience truly bridged the gap between theory and practice.

Expanding knowledge with a podcast for student developers

(Irene’s team working with other student developers (photo taken before COVID-19)

After the event, Irene felt that if a true mentorship network was established among other DSCs in Europe, students would feel more comfortable partnering with one another to talk about common problems they faced. Inspired, she began to build out her mentorship program which included a podcast where student developers could collaborate on projects together.

The podcast, which just released its second episode, also highlights upcoming opportunities for students. In the most recent episode, Irene and friends dive into how to apply for Google Summer of Code Scholarships and talk about other upcoming open source project opportunities. Organizing these types of learning experiences for the community was one of the most fulfilling parts of working as a DSC Lead, according to Irene. She explained that the podcast has been an exciting space that allows her and other students to get more experience presenting ideas to an audience. Through this podcast, Irene has already seen many new DSC members eager to join the conversation and collaborate on new ideas.

As Irene now looks out on her future, she is excited for all the learning and career development that awaits her from the entire Google Developer community. Having graduated from university, Irene is now a Google Developer Groups (GDG) Lead – a program similar to DSC, but created for the professional developer community. In this role, she is excited to learn new skills and make professional connections that will help her start her career.

Are you also a student with a passion for code? Then join a local Google Developer Student Club near you, here.

Coral makes edge AI even more accessible in 2020


Posted by the Coral team

Coral Dev Board Mini and Accelerator Module feature Google's Edge TPU co-processor to accelerate AI at the edge.

Since we launched Coral back in March 2019, we’ve added a number of new product form factors to accommodate the many ways users are adding on-device ML to their products. We’ve also streamlined the ML workflow and added capabilities like model pipelining with multiple Edge TPUs for an easier and more robust developer experience. And from this, we’ve helped enable amazing use cases from smart water meters that prevent water loss with Olea Edge, to systems for improving harvest yield with Farmwave, to noise cancellation in meetings in Google’s own Series One meeting kits.

This week, we’ll begin shipping the Coral Accelerator Module, a multi-chip module that combines the Edge TPU and it’s power circuitry into a solderable package. The module exposes PCIe and USB2 interfaces, which make it even easier to integrate Coral into custom designs. Several companies are already taking advantage of the compact size and capabilities with their new products coming to market. Read more about how Gumstix, STD, Siana Systems and IEI are using our module.

And in December, we’ll begin shipping the Dev Board Mini, a smaller, more power-efficient, and value-oriented board that brings forward a more traditional, flattened single-board computer design. The Dev Board Mini pairs a Mediatek 8167 SoC with the Coral Accelerator Module over USB 2 and is a great way to evaluate the module as the center of a project or deployment.

You can see the new Dev Board Mini and Accelerator Module in action in the latest episode of Level Up, where Markku Lepisto controls his studio lights with speech commands.

To get updates on when the board will be available for purchase and other Coral news, sign up for our newsletter.

Developing for the edge, now simplified

We recently announced a new version of the Coral ML APIs and tools. This release brings the C++ API into parity with Python and makes it more modular, reusable and performant. At the same time it eliminates unnecessary abstractions and surfaces replacing them with native TensorFlow Lite APIs. This release also graduates the Model Pipelining API out of beta and introduces a new model partitioner that automatically partitions models based on profiling and up to 10x better performance.

We’ve added a pre-trained version of MobileDet — a state-of-the-art object detection model for mobile systems — into our models portfolio. We’re migrating our model-development workflow to TensorFlow 2, and we’re including a handful of updated or new models based on the TF2 Keras framework. For details, check out the full announcement on the TensorFlow blog.

We’re also excited to see great developer tools coming from our ecosystem partners. For example, PerceptiLabs offers a visual API for building TensorFlow models and recently published a new demo which trains a machine learning model to identify sign language optimized for the edge with Coral.

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

And SigFox released a radio transceiver board that stacks on either the Coral Dev Board or Dev Board Mini. This allows small data payloads to be transmitted across low power, long range radio networks for use cases like smart cities, fleet management, asset tracking, agriculture and energy. The PCB design will be offered as a free download on SigFox’s website. Google Cloud Solutions Architect Markku Lepisto will present the new design today, in the opening keynote at SigFox Connect.

Customers with a Coral edge

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

Just in time for harvest we wanted to share a story about how Farmwave is using Coral to improve the efficiency of farm equipment and reduce food waste. Traditional yield loss analysis involves hand-counting grains of corn left on the ground mid harvest. It’s a time and labor intensive task, and not feasible for farmers who measure the value of their half-million-dollar combines in minutes spent running them.

By leveraging Coral’s on-device AI capabilities, Farmwave was able to build a system that automates the count while the machine is running. Thus allowing farmers to make real-time adjustments to harvesting machines in response to conditions in the field, which can make a big difference in yield.

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Revolving Sushi Bar in Japan has always been committed to the highest standards of health and safety for its customers. Known for their tech forward approach, Kura has dabbled in sushi making robots, an automated prize machine called Bikkura-pon, and a patented dome-shaped dish cover, aptly dubbed Mr. Fresh. But most recently, Kura has used Coral to develop an AI powered system that not only facilitates efficiency for better customer experiences, but also enables better tracking to prevent foodborne illnesses.

Making AI more accessible

While this year has presented the world with many obstacles, we’ve been impressed by the new ideas and innovations coming forward through technology. By providing the necessary tools and technology for edge AI, we strive to empower society to create affordable, adaptable, and intelligent systems.

We are excited to share all that Coral has to offer as we evolve our platform. For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page.

Please visit Coral.ai to discover more about our edge ML platform and share your feedback at [email protected]. To receive future Coral updates directly in your inbox, sign up for our newsletter.

Doubling down on the edge with Coral’s new accelerator

Posted by The Coral Team

Coral image

Moving into the fall, the Coral platform continues to grow with the release of the M.2 Accelerator with Dual Edge TPU. Its first application is in Google’s Series One room kits where it helps to remove interruptions and makes the audio clearer for better video meetings. To help even more folks build products with Coral intelligence, we’re dropping the prices on several of our products. And for those folks that are looking to level up their at home video production, we’re sharing a demo of a pose based AI director to make multi-camera video easier to make.

Coral M.2 Accelerator with Dual Edge TPU

The newest addition to our product family brings two Edge TPU co-processors to systems in an M.2 E-key form factor. While the design requires a dual bus PCIe M.2 slot, it brings enhanced ML performance (8 TOPS) to tasks such as running two models in parallel or pipelining one large model across both Edge TPUs.

The ability to scale across multiple edge accelerators isn’t limited to only two Edge TPUs. As edge computing expands to local data centers, cell towers, and gateways, multi-Edge TPU configurations will be required to help process increasingly sophisticated ML models. Coral allows the use of a single toolchain to create models for one or more Edge TPUs that can address many different future configurations.

A great example of how the Coral M.2 Accelerator with Dual Edge TPU is being used is in the Series One meeting room kits for Google Meet.

The new Series One room kits for Google Meet run smarter with Coral intelligence

Coral image

Google’s new Series One room kits use our Coral M.2 Accelerator with Dual Edge TPU to bring enhanced audio clarity to video meetings. TrueVoice®, a multi-channel noise cancellation technology, minimizes distractions to ensure every voice is heard with up to 44 channels of echo and noise cancellation, making distracting sounds like snacking or typing on a keyboard a concern of the past.

Enabling the clearest possible communication in challenging environments was the target for the Google Meet hardware team. The consideration of what makes a challenging environment was not limited to unusually noisy environments, such as lunchrooms doubling as conference rooms. Any conference room can present challenging acoustics that make it difficult for all participants to be heard.

The secret to clarity without expensive and cumbersome equipment is to use virtual audio channels and AI driven sound isolation. Read more about how Coral was used to enhance and future-proof the innovative design.

Expanding the AI edge

Earlier this year, we reduced the prices of our prototyping devices and sensors. We are excited to share further price drops on more of our products. Our System-on-Module is now available for $99.99, and our Mini PCIe Accelerator, M.2 Accelerator A+E Key, and M.2 Accelerator B+M key are now available at $24.99. We hope this lower price will make our edge AI more accessible to more creative minds around the world. Later, this month our SoM offering will also expand to include 2 and 4GB RAM options.

Multi-cam with AI

Coral image

As we expand our platform and product family, we continue to keep new edge AI use cases in mind. We are continually inspired by our developer community’s experimentation and implementations. When recently faced with the challenges of multicam video production from home, Markku Lepistö, Solutions Architect at Google Cloud, created this real-time pose-based multicam tool he so aptly dubbed, AI Director.

We love seeing such unique implementations of on-device ML and invite you to share your own projects and feedback at [email protected].

For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page. Please visit Coral.ai to discover more about our edge ML platform.

ML Kit Pose Detection Makes Staying Active at Home Easier

Posted by Kenny Sulaimon, Product Manager, ML Kit; Chengji Yan and Areeba Abid, Software Engineers, ML Kit

ML Kit logo

Two months ago we introduced the standalone version of the ML Kit SDK, making it even easier to integrate on-device machine learning into mobile apps. Since then we’ve launched the Digital Ink Recognition API, and also introduced the ML Kit early access program. Our first two early access APIs were Pose Detection and Entity Extraction. We’ve received an overwhelming amount of interest in these new APIs and today, we are thrilled to officially add Pose Detection to the ML Kit lineup.

ML Kit Overview

A New ML Kit API, Pose Detection

Examples of ML Kit Pose Detection

ML Kit Pose Detection is an on-device, cross platform (Android and iOS), lightweight solution that tracks a subject’s physical actions in real time. With this technology, building a one-of-a-kind experience for your users is easier than ever.

The API produces a full body 33 point skeletal match that includes facial landmarks (ears, eyes, mouth, and nose), along with hands and feet tracking. The API was also trained on a variety of complex athletic poses, such as Yoga positions.

Skeleton image detailing all 33 landmark points

Skeleton image detailing all 33 landmark points

Under The Hood

Diagram of the ML Kit Pose Detection Pipeline

The power of the ML Kit Pose Detection API is in its ease of use. The API builds on the cutting edge BlazePose pipeline and allows developers to build great experiences on Android and iOS, with little effort. We offer a full body model, support for both video and static image use cases, and have added multiple pre and post processing improvements to help developers get started with only a few lines of code.

The ML Kit Pose Detection API utilizes a two step process for detecting poses. First, the API combines an ultra-fast face detector with a prominent person detection algorithm, in order to detect when a person has entered the scene. The API is capable of detecting a single (highest confidence) person in the scene and requires the face of the user to be present in order to ensure optimal results.

Next, the API applies a full body, 33 landmark point skeleton to the detected person. These points are rendered in 2D space and do not account for depth. The API also contains a streaming mode option for further performance and latency optimization. When enabled, instead of running person detection on every frame, the API only runs this detector when the previous frame no longer detects a pose.

The ML Kit Pose Detection API also features two operating modes, “Fast” and “Accurate”. With the “Fast” mode enabled, you can expect a frame rate of around 30+ FPS on a modern Android device, such as a Pixel 4 and 45+ FPS on a modern iOS device, such as an iPhone X. With the “Accurate” mode enabled, you can expect more stable x,y coordinates on both types of devices, but a slower frame rate overall.

Lastly, we’ve also added a per point “InFrameLikelihood” score to help app developers ensure their users are in the right position and filter out extraneous points. This score is calculated during the landmark detection phase and a low likelihood score suggests that a landmark is outside the image frame.

Real World Applications

Examples of a pushup and squat counter using ML Kit Pose Detection

Keeping up with regular physical activity is one of the hardest things to do while at home. We often rely on gym buddies or physical trainers to help us with our workouts, but this has become increasingly difficult. Apps and technology can often help with this, but with existing solutions, many app developers are still struggling to understand and provide feedback on a user’s movement in real time. ML Kit Pose Detection aims to make this problem a whole lot easier.

The most common applications for Pose detection are fitness and yoga trackers. It’s possible to use our API to track pushups, squats and a variety of other physical activities in real time. These complex use cases can be achieved by using the output of the API, either with angle heuristics, tracking the distance between joints, or with your own proprietary classifier model.

To get you jump started with classifying poses, we are sharing additional tips on how to use angle heuristics to classify popular yoga poses. Check it out here.

Learning to Dance Without Leaving Home

Learning a new skill is always tough, but learning to dance without the aid of a real time instructor is even tougher. One of our early access partners, Groovetime, has set out to solve this problem.

With the power of ML Kit Pose Detection, Groovetime allows users to learn their favorite dance moves from popular short-form dance videos, while giving users automated real time feedback on their technique. You can join their early access beta here.

Groovetime App using ML Kit Pose Detection

Staying Active Wherever You Are

Our Pose Detection API is also helping adidas Training, another one of our early access partners, build a virtual workout experience that will help you stay active no matter where you are. This one-of-a-kind innovation will help analyze and give feedback on the user’s movements, using nothing more than just your phone. Integration into the adidas Training app is still in the early phases of the development cycle, but stay tuned for more updates in the future.

How to get started?

If you would like to start using the Pose Detection API in your mobile app, head over to the developer documentation or check out the sample apps for Android and iOS to see the API in action. For questions or feedback, please reach out to us through one of our community channels.