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[JS/TensorFlow] Javascript library that implements machine learning-based models for human pose estimation and human movement analysis. It allows you to easily implement 3 neural network models using for human pose estimation: MoveNet, PoseNet and BlazePose. No node.js required, TensorFlowJS was used, example app is included.

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Javascript / TensorFlow JS, current release: 1.0.0 build 2022-07-20

JS AI BODY TRACKER - tracker.js

JS AI Body Tracker (tracker.js) is a javascript library that implements machine learning-based models for human pose estimation and human movement analysis. Library is written in javascript, it doesn't require node.js. It supports 3 different models for detecting the human pose on the video: MoveNet, PoseNet and BlazePose. Library offers real-time video analysis from three different video sources: web/smartphone camera, video files (mp4, mkv, avi, webm) and online streaming (IPTV/m3u8).

Features

  • easy to implement in your own JS application
  • works in real-time directly in browser
  • works with multiple video sources: web camera, video files (mp4, mkv, avi, webm), online stream (IPTV/m3u8)
  • 3 different neural network models: MoveNet, PoseNet, BlazePose
  • real-time 3D mapping (BlazePose)
  • can be easily extended with events / hooks
  • only one file, for direct import in your own application, no node.js required
  • uses TensorFlow JS, ScatterGL and videoJS libraries

Real-time human pose estimation on MP4 video:

gif1 gif2

Real-time human pose estimation on IPTV/m3u8 online stream:

gif3

Live demo

A demo of a real-time working sample application using the library is here:

https://szczyglis.dev/js-ai-body-tracker

There are 3 input video sources available in the demo application: webcamera, video file and IPTV/m3u8 stream. The entire process of image analysis takes place in real-time.

Basic usage

// javascript

tracker.setModel('MoveNetSinglePoseLightning'); // define model to use

tracker.elCanvas = '#canvas'; // define HTML canvas container
tracker.elVideo = '#video'; // define HTML video container
tracker.el3D = '#view_3d'; // define HTML container for 3D view 

tracker.run('camera'); // run

The repository includes a sample application illustrating the operation and use of the library.

Usage step by step

First, you need to define two HTML elements: video and canvas:

<canvas id="canvas" width="500" height="500"></canvas>
<video id="video" width="500" height="500">
	<source src="">
</video>

Then you need to import the library and configure it:

<script src="./js/tracker.js"></script>
<script>
	tracker.setModel('MoveNetSinglePoseLightning');	
	tracker.elCanvas = '#canvas';
	tracker.elVideo = '#video';
	tracker.run('camera');
</script>

Examples of use

1) VIDEO INPUT: webcam / smartphone camera

<!DOCTYPE html>
<html>
<head>
</head>
<body>
<div class="container">
	<canvas id="canvas" width="500" height="500"></canvas>
	<video id="video" width="500" height="500" style="display:none">
		<source src="">
	</video>				
</div>

<!-- Load Tensor Flow -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/pose-detection"></script>

<!-- Load three.js -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r134/three.min.js"></script>
<!-- Load scatter-gl.js -->
<script src="https://cdn.jsdelivr.net/npm/scatter-gl@0.0.13/lib/scatter-gl.min.js"></script>

<!-- Load tracker.js -->
<script src="./js/tracker.js"></script>
<script>
	tracker.setModel('MoveNetSinglePoseLightning');
	tracker.elCanvas = '#canvas';
	tracker.elVideo = '#video';
	tracker.run('camera');
</script>
</body>
</html>

The above code activates the camera and initializes the model detecting the human pose, and then displays the points detected on the video from camera on the canvas element in real-time, superimposing them on the input from webcamera.

2) VIDEO INPUT: movie file (mp4, mkv, avi, webm)

<!DOCTYPE html>
<html>
<head>
</head>
<body>
<div class="container">
	<canvas id="canvas" width="500" height="500"></canvas>
	<video id="video" width="500" height="500" style="display:none">
		<source src="movie.mp4">
	</video>				
</div>

<!-- Load Tensor Flow -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/pose-detection"></script>

<!-- Load three.js -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r134/three.min.js"></script>
<!-- Load scatter-gl.js -->
<script src="https://cdn.jsdelivr.net/npm/scatter-gl@0.0.13/lib/scatter-gl.min.js"></script>

<!-- Load tracker.js -->
<script src="./js/tracker.js"></script>
<script>
	tracker.setModel('MoveNetSinglePoseLightning');
	tracker.elCanvas = '#canvas';
	tracker.elVideo = '#video';
	tracker.run('video');
</script>
</body>
</html>

The above code loads the movie movie.mp4 and initializes the human pose analysis model, then displays the points detected on the video in real time on canvas, superimposing them on the input video.

3) VIDEO INPUT: online stream (IPTV/m3u8)

<!DOCTYPE html>
<html>
<head>
	<link href="https://cdnjs.cloudflare.com/ajax/libs/video.js/7.0.0/video-js.css" rel="stylesheet">
    <script src="https://cdnjs.cloudflare.com/ajax/libs/video.js/7.0.0/video.min.js"></script>
</head>
<body>
<div class="container">
	<canvas id="canvas" width="500" height="500"></canvas>
	<video id="video" class="video-js vjs-fluid vjs-default-skin" preload="metadata" width="500" height="500" style="display:none">
		<source src="https://multiplatform-f.akamaihd.net/i/multi/will/bunny/big_buck_bunny_,640x360_400,640x360_700,640x360_1000,950x540_1500,.f4v.csmil/master.m3u8">
	</video>				
</div>

<!-- Load Tensor Flow -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/pose-detection"></script>

<!-- Load three.js -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r134/three.min.js"></script>
<!-- Load scatter-gl.js -->
<script src="https://cdn.jsdelivr.net/npm/scatter-gl@0.0.13/lib/scatter-gl.min.js"></script>

<!-- Load tracker.js -->
<script src="./js/tracker.js"></script>
<script>
	tracker.setModel('MoveNetMultiPoseLightning');
	tracker.elCanvas = '#canvas';
	tracker.elVideo = '#video';
	tracker.run('stream');
</script>
</body>
</html>

The above code opens .m3u8 online video stream and initiates the human pose detection model, and then displays the points detected on video stream superimposed on the stream image in the canvas element in real-time. Note that the videoJS library is used to handle the stream.

Configuration

Model

The library has preconfigured settings for 3 different neural network models, you can use these settings or define all options manually. To use the predefined model, use the setModel() method:

tracker.setModel('MoveNetSinglePoseLightning');

Available models:

  • MoveNetSinglePoseLightning
  • MoveNetSinglePoseThunder
  • MoveNetMultiPoseLightning
  • PoseNetMobileNetV1
  • PoseNetResNet50
  • BlazePoseLite
  • BlazePoseHeavy
  • BlazePoseFull

You can also define the settings manually, e.g. MoveNet network and the "multipose" settings:

tracker.detectorModel = poseDetection.SupportedModels.MoveNet;
tracker.detectorConfig = {
    modelType: poseDetection.movenet.modelType.MULTIPOSE_LIGHTNING,
    enableSmoothing: true,
    multiPoseMaxDimension: 256,
    enableTracking: true,
    trackerType: poseDetection.TrackerType.BoundingBox
}
tracker.minScore = 0.35;

Network models differ in efficiency and accuracy, you can read more about the models of these neural networks on the Google website:

https://github.com/tensorflow/tfjs-models/tree/master/pose-detection

Input video source

You can define 3 different video sources:

  • camera
  • video
  • stream

To select a source, call the method run() with the source name passed as argument, e.g .:

tracker.run('camera') // takes video from webcam
tracker.run('video') // takes video from movie file (e.g. mp4)
tracker.run('stream') // takes video from m3u8 online stream

DOM elements configuration

You need to define elements for video, canvas and for 3D output (only available for BlazePose model). To do this, set the appropriate values:

tracker.elCanvas = '#canvas';
tracker.elVideo = '#video';
tracker.el3D = '#view_3d';

Display settings

You can define the size of points drawed on canvas:

tracker.pointWidth = 6; // points connection width
tracker.pointRadius = 8; // point circle radius

Events / hooks

You can define your own functions that will handle, for example, points in the image detected by the neural network, the event / hook system is used for this. To define a hook, use the on() method. For example, to define a hook that will display all detected points in real-time live, write the following code:

tracker.on('beforeupdate', function(poses) {
	console.log(poses);
});

The above code will make the console display a set of points detected in the image when rendering each frame.

You can define hooks for 4 types of events:

beforeupdate - hook executes before canvas render in every frame

afterupdate - hook executes after canvas render in every frame

statuschange - hook executes when status was changed

detectorerror - hook executes when detector error occured

videoerror - hook executes when video/stream error occured

Defining a hook is as follows:

tracker.on('HOOK_TYPE', function(value) {
	// do something with value
});

3D pose estimation

To display the detected points in 3D space, turn on the enable3D option, as shown below:

tracker.enable3D = true;

A HTML element (e.g. div) must also be created, which will act as a container for the 3D output. To do this, define the element as below:

<body>
	...
	<div id="view_3d"></div>

	<script>
		tracker.el3D = '#view_3d';
		tracker.enable3D = true;

3D keypoints are displayed using the ScatterGL library.

3dok

Options reference

tracker.detectorModel - detector instance, default: poseDetection.SupportedModels.MoveNet

tracker.detectorConfig - detector config

tracker.autofit bool, enable autofit/rescale points on canvas CSS auto-scaling, default: false

tracker.enableAI bool, enable or disable tracking, default: true

tracker.enableVideo bool, enable or disable display original video on canvas, default: true

tracker.enable3D bool, enable or disable 3D keypoints, default: false

tracker.pointWidth int, width of line between points, default: 6

tracker.pointRadius int, point circle radius, default: 8

tracker.minScore float, minimum threshold (score) for estimated points, default: 0.35

tracker.log bool, enable logging to console, default: true

tracker.el3D string, HTML element for 3D keypoint, default: #view_3d

tracker.elCanvas string, HTML element for canvas, default: #canvas

tracker.elVideo string, HTML element for video, default: #video

The following options should be defined before calling run() method: detectorModel, detectorConfig, elCanvas, elVideo, el3D.

Live demo: https://szczyglis.dev/js-ai-body-tracker

demo_www

Changelog

- 1.0.0 - published first release (2022-07-20)

Credits

JS AI BODY TRACKER is free to use but if you liked then you can donate project via BTC:

14X6zSCbkU5wojcXZMgT9a4EnJNcieTrcr

or by PayPal: https://www.paypal.me/szczyglinski

Enjoy!

MIT License | 2022 Marcin 'szczyglis' Szczygliński

https://github.com/szczyglis-dev/js-ai-body-tracker

https://szczyglis.dev/js-ai-body-tracker

Contact: szczyglis@protonmail.com

About

[JS/TensorFlow] Javascript library that implements machine learning-based models for human pose estimation and human movement analysis. It allows you to easily implement 3 neural network models using for human pose estimation: MoveNet, PoseNet and BlazePose. No node.js required, TensorFlowJS was used, example app is included.

szczyglis.dev/js-ai-body-tracker

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