Feature extraction in 3D gesture recognition for HMM with Kinect data

Question

I have a set of 3D points mapped onto [0, 1] segments. These points represent simple gestures like circles, waving etc. Now I want to use Hidden Markov Models to recognize my gestures. First step is to extract features for (X, Y, Z) data. I tried to search something useful and found a couple examples: SIFT, SURF, some kind of Fast Fourier Transform etc.

I'm confused which one I should use in my project. I want to recognize gestures using data from Kinect controller, so I don't need to track joints algorithmically.

Answer 1

I had to implement HMM for gesture recognition a year or two ago for a paper on different Machine Learning methods. I came across Accord .NET Framework which helps implement many of those I was looking into, including HMM. It's fairly easy to use and its creator is active on the forums.

To train the HMM I created a Kinect application that would start recording a gesture once a body part was stationary for 3 seconds, it would then record all the points to an output file until said part stopped for 3 seconds again. I then selected the best attempts at the gestures I wanted to train and used them as my training set.

If you are new to Kinect Gesture Recognition and don't need to use HMM I would suggest maybe looking into Template Matching as it's a lot simpler and I found it can be very effective for simple gestures.