NDLI: A probabilistic sensor for the perception of activities

Content Provider	IEEE Xplore Digital Library
Author	Chomat, O. Crowley, J.L.
Copyright Year	2000
Description	Author affiliation: Lab. GRAVIR, INRIA Rhone-Alpes, Montbonnot, France (Chomat, O.)
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.
Starting Page	314
Ending Page	319
File Size	147145
Page Count	6
File Format	PDF
ISBN	0769505805
DOI	10.1109/AFGR.2000.840652
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-03-30
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image sequences Statistics Energy measurement Motion measurement Data mining Gabor filters Information filtering Information filters Probability density function Histograms
Content Type	Text
Resource Type	Article

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