Vehicles Tracking Based on Corner Feature in Video-based ITS
Lei Xie, Guangxi Zhu, Miao Tang, Haixiang Xu, Zhenming Zhang · 2006
Tracking vehicles is an important and challenging issue in video-based intelligent transportation systems and has been broadly investigated in the past. This paper presents a robust and real-time method for tracking vehicles and the proposed algorithm includes two stages: object region extraction, vehicle tracking. Object region extraction is a key step and the concept of tracking vehicle is built upon the vehicle-segmentation method. According to the segmented vehicle shape, we propose a three-step predict method based on Kalman filter to track each vehicle. The proposed method has been tested on a number of monocular traffic-image sequences and the experimental results show that the algorithm is robust and real-time. The correct rate of vehicle tracking is higher than 85 percent, independent of environmental conditions