Real-time vehicle detection and tracking using a mean-shift based blob analysis and tracking approach

Implementing computer vision on traffic scenarios are one of the most widely sought area in the field of vision research. In dealing with the surveillance in traffic scenarios, every vehicle in the scene must be observed which results to problem arising from instances whenever the traffic density in an area is high due to occlusion caused by the large number of vehicles being observed. Thus, this paper proposes a vehicle detection and tracking algorithm whose main purpose is to detect and track vehicles entering an intersection and track them robustly in real-time. The algorithm which was used is a blob analysis and tracking based on a mean-shift kernel. The blob approach acts as the main tracking and will use the mean-shift in the event of blob merging or occlusion. In this paper, the proposed tracking method is tested using a CCTV camera on an intersection with high traffic density to illustrate the capability of solving occlusion and observe the robustness of the algorithm in the scene. The results show that the proposed system successfully tracks the vehicles during and after occlusion with other vehicles or other types of objects in the scene.