A Robust Framework for Vehicle Detection and Tracking Based on Particle Filter
Vehicle detection and tracking have been promising application in traffic surveillance and vehicular network. However, the vision-based approach still remains a challenging task due to the problems of illumination variation, shadow and occlusion. In this paper, we propose a robust framework mainly concatenates on two aspects: adaptive vehicle detection with shadow removal, and vehicle tracking with occlusion handling. Firstly, for vehicle detection stage, an improved ViBe algorithm with ghost suppression is adopted to extract moving vehicle region. Then moving shadow is removed by integrating improved color with texture feature. Then, aiming to achieve the multi-vehicle tracking, we propose an enhanced histogram of the oriented gradient combined with HSV color space based on particle filter (ECHOGPF). Finally, we employ the occlusion detection and occlusion segmentation to refine our system, which are based on one-dimensional maximum entropy and the least square ellipse fitting. Experiments on popular datasets show that our proposed system has a good effectiveness, e.g., the accuracy is 95% on the vehicle tracking.
Vehicle detection/tracking Foreground extraction Particle filter Histogram of oriented gradients HSV color space Occlusion segmentation
Huihui Liu Yong Liu Haiqing Du
Beijing University of Posts and Telecommunications, Beijing, China
国际会议
重庆
英文
803-811
2015-12-18（万方平台首次上网日期，不代表论文的发表时间）