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  • Research Article
  • Open Access

MPEG-2 Compressed-Domain Algorithms for Video Analysis

EURASIP Journal on Advances in Signal Processing20062006:056940

https://doi.org/10.1155/ASP/2006/56940

  • Received: 1 September 2004
  • Accepted: 6 June 2005
  • Published:

Abstract

This paper presents new algorithms for extracting metadata from video sequences in the MPEG-2 compressed domain. Three algorithms for efficient low-level metadata extraction in preprocessing stages are described. The first algorithm detects camera motion using the motion vector field of an MPEG-2 video. The second method extends the idea of motion detection to a limited region of interest, yielding an efficient algorithm to track objects inside video sequences. The third algorithm performs a cut detection using macroblock types and motion vectors.

Keywords

  • Information Technology
  • Vector Field
  • Quantum Information
  • Video Sequence
  • Motion Vector

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Authors’ Affiliations

(1)
Fraunhofer IMK, Schloss Birlinghoven, Sankt Augustin, 53754, Germany

References

  1. Manjunath BS, Salembier P, Sikora T: Introduction to MPEG-7: Multimedia Content Description Interface. John Wiley & Sons, New York, NY, USA; 2002.Google Scholar
  2. Wang R, Huang T: Fast camera motion analysis in MPEG domain. Proceedings of IEEE International Conference on Image Processing (ICIP '99), October 1999, Kobe, Japan 3: 691-694.View ArticleGoogle Scholar
  3. Smolic A, Hoeynck M, Ohm J-R: Low-complexity global motion estimation from P-frame motion vectors for MPEG-7 applications. Proceedings of IEEE International Conference on Image Processing (ICIP '00), September 2000, Vancouver, BC, Canada 2: 271-274.View ArticleGoogle Scholar
  4. Kuhn PM: Camera motion estimation using feature points in MPEG compressed domain. Proceedings of IEEE International Conference on Image Processing (ICIP '00), September 2000, Vancouver, BC, Canada 3: 596-599.Google Scholar
  5. Kobla V, Doermann D, Rosenfeld A: Compressed domain video segmentation. In Tech. Rep. CAR-TR-839 (CS-TR-3688). University of Maryland, College Park, Md, USA; 1996.Google Scholar
  6. Comaniciu D, Meer P: Mean shift analysis and applications. Proceedings of 7th IEEE International Conference on Computer Vision (ICCV '99), September 1999, Kerkyra, Greece 2: 1197-1203.View ArticleGoogle Scholar
  7. Danette Allen B, Bishop G: Tracking: Beyond 15 Minutes of Thought. SIGGRAPGH 2001 Course 11 Booklet, August 2001, Los Angeles, Calif, USAGoogle Scholar
  8. Gyaourova A, Kamath C, Cheung S-C: Block matching for object tracking. In Tech. Rep. UCRL-TR-200271. Lawrence Livermore National Laboratory, Livermore, Calif, USA; 2003.Google Scholar
  9. Kobla V, Doermann D, Lin K-I, Faloutsos C: Feature normalization for video indexing and retrieval. In Tech. Rep. CAR-TR-847 (CS-TR-3732). University of Maryland, College Park, Md, USA; 1996.Google Scholar
  10. Khan JI, Guo Z, Oh W: Motion based object tracking in MPEG-2 stream for perceptual region discriminating rate transcoding. Proceedings of 9th ACM International Conference on Multimedia (ACM Multimedia '01), September–October 2001, Ottawa, Ontario, Canada 572-576.View ArticleGoogle Scholar
  11. Zhang H, Kankanhalli A, Smoliar SW: Automatic partitioning of full-motion video. ACM Multimedia Systems 1993, 1(1):10-28. 10.1007/BF01210504View ArticleGoogle Scholar
  12. Nagasaka A, Tanaka Y: Automatic video indexing and full-video search for object appearances. In Proceedings of IFIP 2nd Working Conference on Visual Database Systems, September–October 1991. North-Holland; 113-127.Google Scholar
  13. Zabih R, Miller J, Mai K: A feature-based algorithm for detecting and classifying scene breaks. Proceedings of 3rd ACM International Conference on Multimedia (ACM Multimedia '95), November 1995, San Francisco, Calif, USA 189-200.View ArticleGoogle Scholar
  14. Bozdagi G, Sencar HT: Preprocessing tool for compressed video editing. Proceedings of IEEE 3rd Workshop on Multimedia Signal Processing, September 1999, Copenhagen, Denmark 283-288.Google Scholar
  15. Bovik A (Ed): Handbook of Image and Video Processing. Academic Press, New York, NY, USA; 2000.MATHGoogle Scholar
  16. Calic J, Izquierdo E: Towards real-time shot detection in the MPEG-compressed domain. Proceedings of Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '01), May 2001, Tampere, Finland 95-100.Google Scholar
  17. Viola P, Jones MJ: Robust real-time object detection. In Tech. Rep. CRL 2001/01. Cambridge Research Laboratory, Cambridge, Mass, USA; 2001.Google Scholar
  18. Chang S-F, Messerschmitt DG: Manipulation and compositing of MC-DCT compressed video. IEEE Journal on Selected Areas in Communications 1995, 13(1):1-11. 10.1109/49.363151View ArticleGoogle Scholar
  19. Shen K, Delp EJ: A fast algorithm for video parsing using MPEG compressed sequences. Proceedings of IEEE International Conference on Image Processing (ICIP '95), October 1995, Washington, DC, USA 2: 252-255.View ArticleGoogle Scholar
  20. Yeo B-L, Liu B: On the extraction of DC sequence from MPEG compressed video. Proceedings of IEEE International Conference on Image Processing (ICIP '95), October 1995, Washington, DC, USA 2: 260-263.View ArticleGoogle Scholar
  21. Rowley HA, Baluja S, Kanade T: Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998, 20(1):23-38. 10.1109/34.655647View ArticleGoogle Scholar
  22. Wang H, Chang S-F: A highly efficient system for automatic face region detection in MPEG video. IEEE Transactions on Circuits and Systems for Video Technology 1997, 7(4):615-628. 10.1109/76.611173MathSciNetView ArticleGoogle Scholar

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