A probabilistic sensor for the perception of activities

Abstract

This paper presents a new technique for the perception of activities using a statistical description of spatio-temporal properties. With this approach, the probability of an activity in a spatio-temporal image sequence is computed by applying a Bayes rule to the joint statistics of the responses of motion energy receptive fields. A set of motion energy receptive fields is designed in order to sample the power spectrum of a moving texture. Their structure relates to the spatio-temporal energy models of Adelson and Bergen where measures of local visual motion information are extracted comparing the outputs of triad of Gabor energy filters. Then the probability density function required for the Bayes rule is estimated for each class of activity by computing multi-dimensional histograms from the outputs from the set of receptive fields. The perception of activities is achieved according to the Bayes rule. The result at a given time is the map of the conditional probabilities that each pixel belongs to an activity of the training set. The approach is validated with experiments in the perception of activities of walking persons in a visual surveillance scenario. Results are robust to changes in illumination conditions, to occlusions and to changes in texture.