基于零树小波的交通视频车辆运动阴影滤除方法  

Vehicle Moving Shadow Removal Approach Based on Zero-Tree Wavelet for Traffic Video

作　　者：王相海[1] 王凯[1] 刘美瑶 苏元贺 宋传鸣[1] 

机构地区：[1]辽宁师范大学计算机与信息技术学院,大连116029

出　　处：《模式识别与人工智能》2016年第12期1104-1113,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.41671439;61402214;41271422);高等学校博士学科点专项科研基金项目(No.20132136110002);辽宁省教育厅科学研究一般项目(No.L2014423)资助~~

摘　　要：基于高斯模型的背景建模方法与简单的背景差分方法很难准确区分运动车辆与阴影.基于此种原因,文中提出基于零树小波的交通视频车辆运动阴影滤除方法.首先将含有噪声的运动前景图像转换至HSV颜色空间.然后对S通道和V通道进行多级下采样小波变换,通过构造运动前景的零树小波掩模,关联不同尺度子带间的系数,使各精细尺度子带掩模的值能得到父子带系数的指导和校正,提高子带自适应阈值的准确性.进一步通过结合阴影的颜色特征,提高判断区域车辆与阴影的区分度.最后通过大量仿真实验验证文中方法的有效性.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.

关 键 词：交通视频车辆 阴影滤除 零树小波掩模 多尺度子带 自适应阈值 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]