Abstract The traditional background modeling method based on Gaussian model and the simple background subtraction method are difficult to accurately distinguish vehicles and shadows. Therefore， a vehicle moving shadow removal approach based on zero-tree wavelet (ZW) for traffic video is proposed in this paper. Firstly, the motion foreground image containing noise is converted to HSV color space and then the S channel and the V channel are processed with multilevel down-sampling wavelet transform. Secondly, by constructing the ZW mask of the motion foreground， the coefficients in different scale subbands are associated, and the mask values of fine scale subband can be guided and corrected by the father sub-band coefficients. Consequently, the accuracy of adaptive threshold of the subband is improved. By combining the shadow color characteristics， the distinction degree of judging vehicles and shadows is improved. A large number of simulation experiments verify the effectiveness of the proposed approach.
Fund:Supported by National Natural Science Foundation of China (No. 41671439,61402214,41271422), Special Research Fund for the Doctoral Program of Higher Education of China (No.20132136110002), Scientific Research General Project of Liaoning Provincial Department of Education (No.L2014423)
About author:: (WANG Xianghai, born in 1965, Ph.D., professor. His research interests include computer graphics and multimedia information processing.) (WANG Kai, born in 1989, master student. His research interests include traffic video information processing.)(LIU Meiyao, born in 1990, master student. Her research interests include intelligent traffic information processing.)(SU Yuanhe, born in 1989, master student. His research interests include traffic image processing.)(SONG Chuanming (Corresponding author), born in 1980, Ph.D., associate professor. His research interests include image and video information processing.)
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2016, Vol. 29 
