Multi-view vehicle detection and tracking in crossroads

Multi-view vehicle detection and tracking in crossroads is of fundamental importance in traffic surveillance yet still remains a very challenging task. The view changes of different vehicles and their occlusions in crossroads are two main difficulties that often fail many existing methods. To handle these difficulties, we propose a new method for multi-view vehicle detection and tracking that innovates mainly on two aspects: the two-stage view selection and the dual-layer occlusion handling. For the two-stage view selection, a Multi-Modal Particle Filter (MMPF) is proposed to track vehicles in explicit view, i.e. frontal (rear) view or side view. In the second stage, for the vehicles in inexplicit views, i.e. intermediate views between frontal and side view, spatial-temporal analysis is employed to further decide their views so as to maintain the consistence of view transition. For the dual-layer occlusion handling, a cluster based dedicated vehicle model for partial occlusion and a backward retracking procedure for full occlusion are integrated complementarily to deal with occlusion problems. The two-stage view selection is efficient for fusing multiple detectors, while the dual-layer occlusion handling improves tracking performance effectively. Extensive experiments under different weather conditions, including snowy, sunny and cloudy, demonstrate the effectiveness and efficiency of our method.