Probabilistic motion parameter models for human activity recognition
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.