Optical flow approximation based motion object extraction for MPEG-2 video stream

Abstract

This paper presents a compressed-domain motion object extraction algorithm based on optical flow approximation for MPEG-2 video stream. The discrete cosine transform (DCT) coefficients of P and B frames are estimated to reconstruct DC ? 2AC image using their motion vectors and the DCT coefficients in I frames, which can be directly extracted from MPEG-2 compressed domain. Initial optical flow is estimated with Black's optical flow estimation framework, in which DC image is substituted by DC ? 2AC image to provide more intensity information. A high confidence measure is exploited to generate dense and accurate motion vector field by removing noisy and false motion vectors. Global motion estimation and iterative rejection are further utilized to separate foreground and background motion vectors. Region growing with automatic seed selection is performed to extract accurate object boundary by motion consistency model. The object boundary is further refined by partially decoding the boundary blocks to improve the accuracy. Experimental results on several test sequences demonstrate that the proposed approach can achieve compresseddomain video object extraction for MPEG-2 video stream in CIF format with real-time performance.

Real-time field sports scene classification using colour and frequency space decompositions

“…Most trends however tend to focus on motion estimation and motion-based object tracking for further parametrization of the video broadcast. In [37], motion object extraction based on optical flow analysis is presented. Among other real-time optical flow-based object extraction, this one seems to be applicable in scenarios where the system has to deal with analysis in compressed domain in real time.…”