A set of motion energy receptive fields is designed in order to sample the power spectrum of a moving texture. 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 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.
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