Crossroad Traffic Surveillance Using Superpixel Tracking and Vehicle Trajectory Analysis

As the density of road traffic increases, it becomes ever more important to analyze the traffic information for improving the transportation safety and reducing vehicle congestion. Vehicle trajectory may provide useful information. This paper presents a novel vehicle trajectory analysis system based on computer vision technology. By utilizing super pixel segmentation and object modeling, the proposed approach is able to increase the robustness of object matching and tracking overtime. We have developed an algorithm which is capable of detecting illegal-left-turn vehicle from a forward-only-lane using the proposed trajectory map constructed based on the vehicles trajectory. Experimental results show that correct object tracking rate is as high as 98.8%. The proposed system can handle illegal-left-turn vehicle detection on the crossroad with superior performance.