Compact representation and probabilistic classification of human actions in videos

This paper addresses the problem of classifying human actions in a video sequence. A representation eigenspace approach based on the PCA algorithm is used to train the classifier according to an incremental learning scheme based on a “one action, one eigenspace” approach. Before dimensionality reduction, a high dimensional description of each frame of the video sequence is constructed, based on foreground blob analysis. Classification is performed by matching incrementally the reduced representation of the test image sequence against each of the learned ones, and accumulating matching scores according to a probabilistic framework, until a decision is obtained. Experimental results with real video sequences are presented and discussed.