A Probabilistic Sensor for the Perception and the Recognition of Activities
This paper presents a new technique for the perception and recognition of activities using statistical descriptions of their spatiotemporal properties. 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 by comparing the outputs of a triad of Gabor energy filters. Then the probability density function required for 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 Bayes rule. The result at each instant of time is the map of the conditional probabilities that each pixel belongs to each one of the activities of the training set. Since activities are perceived over a short integration time, a temporal analysis of outputs is done using Hidden Markov Models.
The approach is validated with experiments in the perception and recognition of activities of people walking in visual surveillance scenari. The presented work is in progress and preliminary results are encouraging, since recognition is robust to variations in illumination conditions, to partial occlusions and to changes in texture. It is shown that it constitute a powerful early vision tool for human behaviors analysis for smart-environnements.
KeywordsHide Markov Model Recognition Rate Activity Recognition Probabilistic Sensor Signal Decomposition
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