Using adaptive background subtraction into a multi-level model for traffic surveillance
Integrated Computer-Aided Engineering
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This paper describes a system for classifying traffic congestion videos based on their observed visual dynamics. Central to the proposed system is treating traffic flow identification as an instance of dynamic texture classification. More specifically, a recent discriminative model of dynamic textures is adapted for the special case of traffic flows. This approach avoids the need for segmentation, tracking and motion estimation that typify extant approaches. Classification is based on matching distributions (or histograms) of spacetime orientation structure. Empirical evaluation on a publicly available data set shows high classification performance and robustness to typical environmental conditions (e.g., variable lighting).