Accurate vehicle location method at an intersection based on distributed video networks
YANG Deliang1,2, XIE Xudong1, Li Chunwen1, NIU Xiaotie2
1. Department of Automation, Tsinghua University, Beijing 100084, China;
2. Department of Mechanical and Electrical Engineering, Beijing Polytechnic College, Beijing 100042, China
Abstract：A robust framework is given for precise vehicle localization in intersections using distributed video networks. Each intersection is equipped with short-range and long-range cameras in a distributed video network. If the vehicle is in the shooting range of the short-range camera, within the region of interest for vehicle identification, and the license plate is perpendicular to the road plane, a vehicle license plate model is used to accurately locate the vehicle position. If the vehicle is in the shooting range of the long-range camera, a pyramid sparse optical flow algorithm with LBP texture features is used in real-time to track the local feature points on the vehicle to estimate the vehicle position based on stable feature points obtained from the similar motions. Finally, information is exchanged between the cameras, a weighted consensus information fusion algorithm is used to obtain a globally optimal estimate of the vehicle position. Tests show that this method can accurately locate the vehicle position at intersections.
杨德亮, 谢旭东, 李春文, 牛小铁. 基于分布式视频网络的交叉口车辆精确定位方法[J]. 清华大学学报（自然科学版）, 2016, 56(3): 281-286,293.
YANG Deliang, XIE Xudong, Li Chunwen, NIU Xiaotie. Accurate vehicle location method at an intersection based on distributed video networks. Journal of Tsinghua University(Science and Technology), 2016, 56(3): 281-286,293.
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2016, Vol. 56 
