H264视频压缩标准以其优良的压缩效率和编码灵活性得到了广泛的应用。提出了一种基于H264压缩域的运动对象分割方法，首先从压缩视频中提取运动场，采用加权中值滤波方法滤除运动场的噪声矢量，再运用后向估计的方法重建预测运动场并进行运动场的累积，然后基于幅度、散度和旋度3个运动特征, 采用改进的统计区域合并方法将运动对象分割出来。实验结果表明，该方法可有效地从H264压缩视频中提取运动对象且分割质量较好。

The H264 video compression standard is extensively applied thanks to its excellent compression efficiency and coding flexibility. A moving object segmentation approach in H264 compressed domain is proposed in this paper. The motion fields are first extracted from the compressed video, in which the noise vectors are removed by weighted median filter. Then the predicted motion fields reconstructed by backward estimation are used to accumulate the motion field. After that, the modified statistical region merging is exploited to segment the moving object based on three motion characteristics magnitude, divergence and curl. Experimental results demonstrate that our approach can efficiently extract the moving objects from H264 compressed and as the segmentation quality is good.