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Real-time spatiotemporal segmentation of video objects in the H. 264 compressed domain

机译：H.264压缩域中视频对象的实时时空分割

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摘要

This paper presents a real-time spatiotemporal segmentation approach to extract video objects in the H.264 compressed domain. The only exploited segmentation cue is the motion vector (MV) field extracted from the H.264 compressed video. MV field is first temporally and spatially normalized and then accumulated by an iteratively backward projection scheme to enhance the salient motion. Then global motion compensation is performed on the accumulated MV field, which is also moderately segmented into different motion-homogenous regions by a modified statistical region growing algorithm. The hypothesis testing using the block residuals of global motion compensation is employed for intra-frame classification of segmented regions, and the projection is exploited for inter-frame tracking of previous video objects. Using the above results of intra-frame classification and inter-frame tracking as input, a correspondence matrix based spatiotemporal segmentation approach is proposed to segment video objects under different situations including appearing and disappearing objects, splitting and merging objects, stopping moving objects, multiple object tracking and scene change in a unified and efficient way. Experimental results for several H.264 compressed video sequences demonstrate the real-time performance and good segmentation quality of the proposed approach.
机译：本文提出了一种实时时空分割方法来提取H.264压缩域中的视频对象。唯一利用的分割提示是从H.264压缩视频中提取的运动矢量（MV）字段。 MV场首先在时间和空间上进行归一化，然后通过迭代后向投影方案进行累积，以增强突出运动。然后，对累积的MV场执行全局运动补偿，该场也通过改进的统计区域增长算法被适度分割为不同的运动均匀区域。使用全局运动补偿的块残差进行的假设检验用于分割区域的帧内分类，并且将投影用于先前视频对象的帧间跟踪。利用以上帧内分类和帧间跟踪的结果作为输入，提出了一种基于对应矩阵的时空分割方法，可以对视频对象在不同情况下进行分割，包括出现和消失的对象，分裂与合并的对象，停止移动的对象，多个对象。以统一高效的方式跟踪和更改场景。几个H.264压缩视频序列的实验结果证明了该方法的实时性能和良好的分割质量。

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