I'm approaching at pattern recognition with HMM (with c++ or python).
My data are x and y coordinate (normalized between -1,1) of the hand in a recorded video and I want to recognize.
This is what I have understand to classify a hand that make a gesture (for example a circle):
- I need some train data (for example N different video with an hand that make a circle) of the same size (X coordinate (x,y) of the hand)
- I have to initialize one HMM (called HMM_circle) with BAUM-WELCH algorithms (or something interative EM-based algorithm) and find the parameters of HMM_circle.
- HMM_circle has X observed states and Y hidden states.
- Record some new observation (some new coordinate of a gesture to classify) new_OBS. Size of new_OBS must be X.
- Find the likelihood of new_OBS given the hidden markov model HMM_circle with BACKWARD-FOREWARD algorithm. This likelihood is a number between 0 and 1. If it is major than a threashold (for example 0.8) it is classified as a circle.
Now, I have something not clear.
How many hidden states in my HMM_circle (Y)? how long the train data (X)? How many train data (N)? Why?
Sorry, I've just began to learn machine learning.
Best Answer
You sure chose a tough problem to learn machine learning. I don't know if you've read the literature so I'm going to recommend these papers, having used his system.