一种压缩域中的快速运动目标提取算法

A Fast Compressed Domain Approach for Moving Object Extraction

论文提出了一种工作于MPEG压缩域的快速运动目标提取算法。算法以通过部分解码得到的运动向量和亮度分量的直流DCT系数作为输入,提取P帧的运动目标。首先采用鲁棒性回归分析估计全局运动,标记出与全局运动不一致的宏块,得到运动块的分布;然后将运动向量场插值作为时间域的特征,将重构的直流图像转换到LUV颜色空间作为空间域的特征,采用快速平均移聚类找到时间和空间特征具有相似性的区域,得到细化的区域边界;最后结合运动块分布和聚类分析的结果,通过基于马尔可夫随机场的统计标号方法进行背景分离,得到运动目标的掩模。实验结果表明该算法可以有效地消除运动向量噪声的影响,并有很高的处理速度,对于CIF格式的视频码流,每秒可以处理约50帧。

A fast moving object extraction method working in MPEG compressed domain is proposed in this paper.The embedded motion vectors and DC coefficients of DCT transformation in an MPEG-like coded video stream are acquired by partially decoding and used as input to the algorlthm.The moving object mask for each P frame is extracted.Firstly,the global motion between current frame and the last reference frame is estimated by robust regression analysls,and blocks non-conforming to the estimated global motion are marked as potential moving blocks;Then,taking the upsampled motion vector field as the temporal space feature,and the reconstructed DC image converted to LUV color space as the spatial space feature,a fast mean-shift based clustering procedure is performed to find regions with similar temporal and spatial characteristics and get finer region boundaries;Finally,the moving object mask is obtained by an MRF - based statistical labeling procedure.The experimental results show that the proposed algorithm can effectively suppress the influence of motion vector noises,and has a very fast processing speed.For CIF video streams,the algorithm can run at a speed of 50 frames per second.