REAL-TIME COMPRESSED-DOMAIN SPATIOTEMPORAL VIDEO SEGMENTATION

In this paper, a novel algorithm for the real-time, unsupervised segmentation of image sequences in the compressed domain is proposed. The algorithm utilizes the motion information present in the compressed stream in the form of P-frame forward motion vectors, as well as basic color information in the form of DC coefficients present in I-frames. An iterative rejection scheme based on the bilinear motion model is used for performing foreground/background segmentation. Further examining the temporal consistency of the output of iterative rejection, clustering to connected regions and performing region tracking, results to foreground spatiotemporal objects being formed. Background segmentation to spatiotemporal objects is also performed. Experimental results on known sequences demonstrate the efficiency of the proposed approach and reveal the potential of employing it in content-based applications such as objectbased video indexing and retrieval.

[1]  R. Venkatesh Babu,et al.  Compressed domain motion segmentation for video object extraction , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Michael G. Strintzis,et al.  A framework for the efficient segmentation of large-format color images , 2002, Proceedings. International Conference on Image Processing.

[3]  Michael G. Strintzis,et al.  Segmentation and Content-Based Watermarking for Color Image and Image Region Indexing and Retrieval , 2002, EURASIP J. Adv. Signal Process..

[4]  Thomas Sikora,et al.  The MPEG-7 visual standard for content description-an overview , 2001, IEEE Trans. Circuits Syst. Video Technol..

[5]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[6]  Tianli Yu,et al.  Retrieval of video clips using global motion information , 2001 .

[7]  Ya-Qin Zhang,et al.  A confidence measure based moving object extraction system built for compressed domain , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[8]  Gagan B. Rath,et al.  Iterative least squares and compression based estimations for a four-parameter linear global motion model and global motion compensation , 1999, IEEE Trans. Circuits Syst. Video Technol..

[9]  David Doermann,et al.  Archiving, indexing, and retrieval of video in the compressed domain , 1996, Other Conferences.

[10]  Kai-Kuang Ma,et al.  Spatiotemporal segmentation of moving video objects over MPEG compressed domain , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[11]  M. G. Strintzis,et al.  Fast Compressed-domain Segmentation for Video Indexing and Retrieval , 2002 .

[12]  Lorenzo Favalli,et al.  Object tracking for retrieval applications in MPEG-2 , 2000, IEEE Trans. Circuits Syst. Video Technol..

[13]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[14]  O. Sukmarg,et al.  Fast object detection and segmentation in MPEG compressed domain , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).