A High Precision Compressed Domain Approach for Video Object Segmentation
一种高精度的压缩域视频目标分割算法

A fast video object segmentation method working in MPEG compressed domain is presented in this paper. Moving object masks in P frames are extracted by exploiting features obtained by partial decoding. To increase object boundary precision, for each P frame, a 1/16 sub image is constructed using DC and three AC coefficients, and motion compensation information, then a fast mean shift clustering algorithm is used to divide the image into regions with coherence luminance and obtain high precision region boundaries. For reducing the influence of motion vector noise, a MRF-based statistical labeling method is exploited to classify regions into two classes: moving object and background. The proposed algorithm can get a boundary precision of 4×4 sub-block with a high processing speed. For CIF video streams, the algorithm can run at a speed of 40 frames per second in a Pentium IV 2GHz platform.