A novel 2D-3D hybrid approach to vehicle trajectory and speed estimation
We present in this paper a novel surveillance system using calibrated stereo camera pair. The system adopts a 2D-3D hybrid approach where vehicle detection and tracking are first performed in the 2D space. Then, both appearance and depth cues are incorporated into the tracking module based on a hybrid 2D-3D approach. After change detection is carried out in the original image domain, moving vehicles can be detected and tracked over time. Vehicle positions and speeds can be estimated very accurately by first retrieving the 3D coordinates of pixels associated with the tracked vehicles resulting in a 3D vehicle point cloud and fitting a cuboid to it. The proposed stereo vehicle surveillance system is capable of extracting several important driving parameters, including vehicle trajectories, speeds, and orientations. Experimental results have confirmed the efficiency and robustness of the system and its applicability to road safety applications, including speeding and drunk driving detection.