An efficient compressed domain moving object segmentation algorithm based on motion vector field

• Published in: Journal of Shanghai University Vol. 12; no. 3; pp. 221 - 227
• Main Author: 刘志 沈礼权 张兆杨
• Format: Journal Article
• Language: English
• Published: Heidelberg Shanghai University Press 01.06.2008
• Subjects: Classical Mechanics; Engineering; Environment; Life Sciences; Materials Science; Mathematical and Computational Engineering; Mechatronics; 分割算法; 压缩域; 运动对象; 运动矢量场; compressed domain segmentation; motion vector (MV) field; moving object segmentation
• ISSN: 1007-6417; 1863-236X
• DOI: 10.1007/s11741-008-0307-2

In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.