Handtracking

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Handtracking is an Open Source Data & Analytics tool that provides a real-time hand detector using neural networks (SSD) on TensorFlow. It offers code and models for adapting hand detection to various use cases, including browser-based applications.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is handtracking?

Handtracking is an open-source GitHub repository that details the process and provides scripts for training a real-time hand detector using Neural Networks (SSD) on TensorFlow. It leverages the TensorFlow Object Detection API and focuses on detecting hands, particularly from an egocentric viewpoint. The project emphasizes the importance of dataset preparation, using the Egohands Dataset, and demonstrates transfer learning with models like ssd_mobilenet_v1_coco. It includes code for data cleaning, conversion to TFRecord format, and training, along with pre-trained models. The repository also highlights real-time detection capabilities on various devices and provides updates for browser-based hand tracking via Handtrack.js and Android integration using TFLite models.

Best used for

Ideal for developers and data scientists who need to implement real-time hand detection, train custom object detectors, and integrate hand tracking into web or mobile applications. Especially valuable for those working with egocentric view data or exploring computer vision research.

Common actions

detect hands

train neural networks

process image data

implement computer vision

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Capabilities

Key features

Real-time hand detection
Neural network training
TensorFlow Object Detection API
Dataset preparation scripts
Transfer learning support
Pre-trained models
Browser integration (Handtrack.js)

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What datasets does Handtracking use for training?

Handtracking primarily uses the Egohands Dataset, which provides high-quality, pixel-level annotations of hands across various environments and activities. The repository includes scripts to clean and convert this data into the TFRecord format required for TensorFlow training.

Can I use Handtracking for real-time applications?

Yes, Handtracking is designed for real-time hand detection. The repository demonstrates its performance on video streams from webcams and YouTube videos, achieving various frames per second (FPS) depending on image size and hardware, even on a MacBook Pro CPU.

Does Handtracking support browser-based applications?

Yes, Handtracking supports browser-based applications through Handtrack.js. The model has been exported using the Tensorflow.js converter, allowing for hand tracking in just a few lines of JavaScript code directly in the browser without installation or model training.

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