Integrating Behavior-based Prediciton for Tracking Vehicles in Traffic Videos

Abstract

Road vehicles usually remain within marked lanes. Such an hypothesis reflects a longer temporal perspective than the frequently used assumption that a vehicle continues with the currently estimated speed and direction. We study the first, more general, hypothesis in particular to track road vehicles through extended periods of occlusion "without", however, relying on 3D-models of occluding foreground bodies. A potential onset of occlusion is detected by a fuzzy conjunction of large, "facet-specific" color changes and a low ratio of the number of pixels with a prediction-compatible Optical-Flow (OF) vector relative to the total number of pixels within a facet of the 3D-polyhedral vehicle model. Experimental results for the entire approach are presented.