matlab - Gesture recognition using hidden markov model -

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i working on gesture recognition application, using hidden markov model classification stage on matlab(using webcam). i've completed pre-processing part includes extraction of feature vector. i've applied principal component analysis(pca) these vectors.

now me use kevin murphy's hmm toolbox, need observation sequence in form of numbers(integers) ranging 1 m (m = number of observation symbols). if i'm correct have use concept of codebook , use vector quantization observation sequence.

my questions:

  1. how build codebook?
  2. and how use codebook obtain observation symbols of input video?

note: i've used elliptical fourier descriptors shape feature extraction , each gesture pca values stored in matrix of dimension [11x220] (number of frames in video = 11)

what do next? there other way obtain feature vectors instead of elliptical fourier descriptors?

an hmm family of probabilistic models sequential data in assume data generated discrete-state markov chain on latent ("hidden") state space. generally, so-called "emissions" come same family of distributions each state, different parameters.

i'm not particularly familiar matlab implementation, sounds you're referring implementation using multinomial emission distribution, observed data sequence of symbols pre-specified alphabet. unknown parameters in model transition probabilities between hidden states , multinomial weights each output symbol in each state. appropriate distribution if features binary , mutually exclusive -- "gesture went left" vs. "gesture went right" or something.

but if features continuous, might more appropriate use continuous emissions distribution instead. instance, gaussian hmms pretty common. here observed data sequence of continuous (possible multivariate) data, , assumption in each hidden state, output i.i.d gaussian mean , (co)variance hope learn.

if you're not opposed python, there nice documentation of both multinomial , gaussian hmms on scikits-learn page: http://scikit-learn.org/stable/modules/hmm.html.

from practical perspective, if you're tied using multinomial hmm on data, suggest building codebook first running k-means clustering , using state labels input hmm. using gaussian hmm preferable.


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