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Progressive Motion Vector Clustering for Motion Estimation and Auxiliary Tracking

Published:05 February 2015Publication History
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Abstract

The motion vector similarity between neighboring blocks is widely used in motion estimation algorithms. However, for nonneighboring blocks, they may also have similar motions due to close depths or belonging to the same object inside the scene. Therefore, the motion vectors usually have several kinds of patterns, which reveal a clustering structure. In this article, we propose a progressive clustering algorithm, which periodically counts the motion vectors of the past blocks to make incremental clustering statistics. These statistics are used as the motion vector predictors for the following blocks. It is proved to be much more efficient for one block to find the best-matching candidate with the predictors. We also design the clustering based search with CUDA for GPU acceleration. Another interesting application of the clustering statistics is persistent static object tracking. Based on the statistics, several auxiliary tracking areas are created to guide the object tracking. Even when the target object has significant changes in appearance or it disappears occasionally, its position still can be predicted. The experiments on Xiph.org Video Test Media dataset illustrate that our clustering based search algorithm outperforms the mainstream and some state-of-the-art motion estimation algorithms. It is 33 times faster on average than the full search algorithm with only slightly higher mean-square error values in the experiments. The tracking results show that the auxiliary tracking areas help to locate the target object effectively.

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References

  1. S. Avidan. 2004. Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 26, 8, 1064--1072. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. K. Chen, Z. Zhou, and W. Wu. 2012. Clustering based search algorithm for motion estimation. In Proceedings of IEEE International Conference on Multimedia and Expo (ICME'12). IEEE, 622--627. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Z. Chen, P. Zhou, and Y. He. 2002. Fast integer pel and fractional pel motion estimation for JVT. In Proceedings of the 6th Meeting of JVT-F017. Joint Video Team of ISO/IEC MPEG & ITU-T VCEG. 5--13.Google ScholarGoogle Scholar
  4. D. Comaniciu and V. Ramesh. 2000. Mean shift and optimal prediction for efficient object tracking. In Proceedings of the IEEE International Conference on Image Processing (ICIP'00). IEEE, 70--73.Google ScholarGoogle Scholar
  5. D. Comaniciu, V. Ramesh, and P. Meer. 2000. Real-time tracking of non-rigid objects using mean shift. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'00). IEEE, 2, 142--149.Google ScholarGoogle Scholar
  6. R. Cucchiara, A. Prati, and R. Vezzani. 2003. Object segmentation in videos from moving camera with MRFs on color and motion features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'03). IEEE, 405--410. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D. L. Davies and D. W. Bouldin. 1979. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1, 2, 224--227. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. W. Du and J. Piater. 2008. A probabilistic approach to integrating multiple cues in visual tracking. In Proceedings of the 10th European Conference on Computer Vision (ECCV'08). Springer, 225--238. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. M. Ester, H. P. Kriegel, J. Sander, and X. Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. AAAI, 96, 226--231.Google ScholarGoogle Scholar
  10. M. Gelgon and P. Bouthemy. 2000. A region-level motion-based graph representation and labeling for tracking a spatial image partition. Pattern Recognit. 33, 4, 725--740.Google ScholarGoogle ScholarCross RefCross Ref
  11. H. Grabner and H. Bischof. 2006. On-line boosting and vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'06). IEEE, 260--267. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. B. Han, S. W. Joo, and L. S. Davis. 2007. Probabilistic fusion tracking using mixture kernel-based Bayesian filtering. In Proceedings of the 11th International Conference on Computer Vision (ICCV'07). IEEE, 1--8.Google ScholarGoogle Scholar
  13. Y. W. Huang, S. Y. Ma, C. F. Shen, and L. G. Chen. 2003. Predictive Line Search: an efficient motion estimation algorithm for MPEG-4 encoding systems on multimedia processors. IEEE Trans. Circ. Syst. Video Tech. 13, 1, 111--117. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. C. Hennebert, V. Rebuffel, and P. Bouthemy. 1996. In Proceedings of the 13th IEEE International Conference on Pattern Recognition (ICPR'96). IEEE, 218--222. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. T. Koga, K. Linuma, A. Hirano, Y. Iijima, and T. Ishiguro. 1981. Motion-compensated interframe coding for video conferencing. In Proceedings of the National Telecommunication Conference. IEEE, G5.3.1--G5.3.5.Google ScholarGoogle Scholar
  16. E. Krause. 1987. Taxicab Geometry: An Adventure in Non-Euclidean Geometry. Dover Publications, New York.Google ScholarGoogle Scholar
  17. J. Kwon and K. M. Lee. 2010. Visual tracking decomposition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'10). IEEE, 1269--1276.Google ScholarGoogle Scholar
  18. R. Li, B. Zeng, and M. L. Liou. 1994. A new three-step search algorithm for block motion estimation. IEEE Trans. Circ. Syst. Video Technol. 4, 4, 438--442. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. J. MacQueen. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, 1, 14, 281--297.Google ScholarGoogle Scholar
  20. S. J. McKenna, Y. Raja, and S. Gong. 1999. Tracking color objects using adaptive mixture models. Image Vision Comput. 17, 3, 225--231.Google ScholarGoogle ScholarCross RefCross Ref
  21. K. Nummiaro, E. Koller-Meier, and L. Van Gool. 2003. An adaptive color-based particle filter. Image Vision Comput. 21, 1, 99--110.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. K. Okuma, A. Taleghani, N. De Freitas, J. J. Little, and D. G. Lowe. 2004. A boosted particle filter: Multitarget detection and tracking. In Proceedings of the 8th European Conference on Computer Vision (ECCV'04). Springer, 28--39.Google ScholarGoogle Scholar
  23. L. M. Po and W. C. Ma. 1996. A novel four-step search algorithm for fast block motion estimation. IEEE Trans. Circ. Syst. Video Technol. 6, 3, 313--317. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. M. Porto, C. Cristani, P. Dall'Oglio, M. Grellert, J. Mattos, S. Bampi, and L. Agostini. 2013. Iterative random search: a new local minima resistant algorithm for motion estimation in high-definition videos. Multimedia Tools Appl. 63, 1, 107--127. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. D. A. Ross, J. Lim, R. S. Lin, and M. H. Yang. 2008. Incremental learning for robust visual tracking. Int. J. Computer Vision 77, 1--3, 125--141. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Z. Shi, W. A. C. Fernando, and D. V. S. De Silva. 2010. A motion estimation algorithm based on predictive intensive direction search for H. 264/AVC. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME'10). IEEE, 667--672.Google ScholarGoogle ScholarCross RefCross Ref
  27. Z. Shi, W. A. C. Fernando, and A. Kondoz. 2011. Adaptive direction search algorithms based on motion correlation for block motion estimation. IEEE Trans. Consum. Electron. 57, 3, 1354--1361.Google ScholarGoogle ScholarCross RefCross Ref
  28. H. Tao, H. S. Sawhney, and R. Kumar. 2002. Object tracking with Bayesian estimation of dynamic layer representations. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1, 75--89. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Y. W. Wu, B. Ma, and P. Li. 2012. A variational method for contour tracking via covariance matching. Science China Info. Sci. 55, 11, 2611--2623.Google ScholarGoogle Scholar
  30. Xiph.org. 2013. Xiph.org video test media (derf's collection). http://media.xiph.org/video/derf/.Google ScholarGoogle Scholar
  31. A. Yilmaz, O. Javed, and M. Shah. 2006. Object tracking: A survey. ACM Comput. Surv. 38, 4, 13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Z. Yin and R. T. Collins. 2008. Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08). IEEE, 1--8.Google ScholarGoogle Scholar
  33. Y. Zhou, Z. Zhou, K. Chen, and W. Wu. 2012. Persistent object tracking in road panoramic videos. In Proceedings of the 13th Pacific Rim Conference on Multimedia (PCM'12). Springer, 359--368. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. C. Zhu, X. Lin, L. P. Chau, K. P. Lim, H. A. Ang, and C. Y. Ong. 2001. A novel hexagon-based search algorithm for fast block motion estimation. In Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'01). IEEE, 1593--1596. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. S. Zhu and K. K. Ma. 2000. A new diamond search algorithm for fast block-matching motion estimation. IEEE Trans. Image Process. 9, 2, 287--290. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Transactions on Multimedia Computing, Communications, and Applications
        ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 11, Issue 3
        January 2015
        173 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/2733235
        Issue’s Table of Contents

        Copyright © 2015 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 5 February 2015
        • Accepted: 1 September 2014
        • Revised: 1 July 2014
        • Received: 1 March 2014
        Published in tomm Volume 11, Issue 3

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