Real-time vehicles tracking based on Kalman filter in an ITS
Xiaohong Zou, Dongmei Li, Jichuan Liu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Tracking vehicles is an important and challenging problem in video-based Intelligent Transportation Systems, which has been broadly investigated in the past. A robust and real-time method for tracking vehicles is presented in this paper. The proposed algorithm includes two stages: vehicle detection, vehicle tracking. Vehicle detection is a key step. The concept of tracking vehicle is built upon the vehicle-segmentation method. According to the segmented vehicle shape, a predict method based on Kalman filter is proposed. By assuming that the vehicle moves with a constant acceleration from the current frame to the next, a Kalman filter model is used to tracking and predicting the trace of a vehicle. The model can be used in the real traffic environment, and can track multi-targets in a big area. So it is practical in the vehicle tracking. 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 can meet the real-time requirement.