Probabilistic Motion Parameter Models for Human Activity Recognition

Xinding Sun, Ching-Wei Chen, B. S. Manjunath

 Dept. of Electrical and Computer Engineering
University of California at Santa Barbara,
Santa Barbara, CA 93106
E-mail: {xdsun, cwei, manj}@ece.ucsb.edu

Abstract

A novel method for human activity recognition is
presented. Given a video sequence containing human
activity, the motion parameters of each frame are first
computed using different motion parameter models. The
likelihood of these observed motion parameters is
optimally approximated, based directly on a multivariate
Gaussian probabilistic model. The dynamic change of
motion parameter likelihood in a video sequence is
characterized using a continuous density hidden Markov
model. Activity recognition is then posed as a motion
parameter maximum likelihood estimation problem.
Experimental results show that the method proposed here
works well in recognizing such complex human activities
as sitting, getting up from a chair, and some martial art
actions.