20 May 2009 Real-time motive vehicle detection with adaptive background updating model and HSV colour space
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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.
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Rong-hui Zhang, Yang Bai, Hong-guang Jia, Tao Chen, "Real-time motive vehicle detection with adaptive background updating model and HSV colour space", 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; https://doi.org/10.1117/12.828709
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