This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the Oxford Hands Dataset (the results were not good). I then tried the Egohands Dataset which was a much better fit to my requirements.
The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases.
Content of this document
Motivation - Why Track/Detect hands with Neural Networks
Data preparation and network training in Tensorflow (Dataset, Import, Training)
Training the hand detection Model
Using the Detector to Detect/Track hands
Thoughts on Optimizations