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Proceedings Paper

Real-time motive vehicle detection with adaptive background updating model and HSV colour space
Author(s): Rong-hui Zhang; Yang Bai; Hong-guang Jia; Tao Chen
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Paper Abstract

In the transportation monitor system, motive vehicle detection by adopting digital image is one of key technologies. To detect motive vehicle accurately, we establish an adaptive background updating model firstly. Noise is suppressed by using modality filter, and we obtain binary image by using maximum entropy to choose dynamic adaptive threshold. Based on positive information of shadow and aspect feature of motive vehicle, we adopt HSV colour space and double threshold to solve the problem of vehicle shadow. According to prediction result of Kalman filtering; we set up the area of interest (AOI) of the vehicle model and adjust the size of AOI dynamically in order to track vehicle accurately. The results of experiment show that, the arithmetic proposed in the paper can suppress shadow availably, detect motive vehicle accurately and satisfy real-time motive vehicle tracking.

Paper Details

Date Published: 20 May 2009
PDF: 5 pages
Proc. SPIE 7283, 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 72832E (20 May 2009); doi: 10.1117/12.828709
Show Author Affiliations
Rong-hui Zhang, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Graduate School of the Chinese Academy of Sciences (China)
Yang Bai, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Graduate School of the Chinese Academy of Sciences (China)
Hong-guang Jia, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Tao Chen, Changchun Institute of Optics, Fine Mechanics and Physics (China)


Published in SPIE Proceedings Vol. 7283:
4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment
Yudong Zhang; James C. Wyant; Robert A. Smythe; Hexin Wang, Editor(s)

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