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Proceedings Paper

Real-time vehicles tracking based on Kalman filter in an ITS
Author(s): Xiaohong Zou; Dongmei Li; Jichuan Liu
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Paper Abstract

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.

Paper Details

Date Published: 5 March 2008
PDF: 7 pages
Proc. SPIE 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing, 662306 (5 March 2008); doi: 10.1117/12.791270
Show Author Affiliations
Xiaohong Zou, Yanshan Univ. (China)
Dongmei Li, Yanshan Univ. (China)
Jichuan Liu, Yanshan Univ. (China)

Published in SPIE Proceedings Vol. 6623:
International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing
Liwei Zhou, Editor(s)

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