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车辆阴影分割是智能交通领域中车辆识别的一项重要内容，阴影分割的好坏直接影响到车辆识别的准确性以及整个智能交通监控系统的性能。针对当前基于RGB和HSV颜色空间的车辆阴影分割算法缺陷与不足，本文提出了一种新的基于YCbCr空间的车辆阴影分割算法。首先选取图像中的运动区域，运动区域包括车辆以及阴影；然后根据阴影区域出现的特点，选择初始阴影数据；最后，通过本文提出的阴影分割算法最终确定阴影区域的形状与位置。经过实际道路运行测试，该算法能提取出的车辆阴影完整性好，具有较好的鲁棒性，在智能交通领域具有一定的应用价值与前景。车辆阴影分割是智能交通领域中车辆识别的一项重要内容，阴影分割的好坏直接影响到车辆识别的准确性以及整个智能交通监控系统的性能。针对当前基于RGB和HSV颜色空间的车辆阴影分割算法缺陷与不足，本文提出了一种新的基于YCbCr空间的车辆阴影分割算法。首先选取图像中的运动区域，运动区域包括车辆以及阴影；然后根据阴影区域出现的特点，选择初始阴影数据；最后，通过本文提出的阴影分割算法最终确定阴影区域的形状与位置。经过实际道路运行测试，该算法能提取出的车辆阴影完整性好，具有较好的鲁棒性，在智能交通领域具有一定的应用价值与前景。

Vehicle Shadow Segmentation Algorithm in Complex Environment

Vehicle shadow segmentation is one of the most important parts of vehicle detection in the field of intelligent traffic monitoring. Shadow segmentation directly influences the veracity of vehicle recognition even the performance of the whole monitoring system. To cover the shortages of RGB or HSV color space based vehicle shadow segmentation algorithms, this paper puts forward a new shadow segmentation algorithm based on YCbCr color space. First, the motion area which includes the vehicle and the shadow is selected, and then the original data of the shadow according to the characteristics of the occurrence of shadow is chosen, finally, the shape and location of the shadow region is determined by the YCbCr shadow segmentation algorithm. Actual road test shows that the integral vehicle shadow can be obtained by the proposed algorithm. The algorithm with better robustness has a practical value in the field of intelligent traffic monitoring.