Probabilistic Tracking Method Based on the Spatial Bin-Ratio Information

REFERENCES

• Adam , Amit , Ehud Rivlin , and Ilan Shimshoni . 2006 . Robust fragments-based tracking using the integral histogram. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 798–805 .
   Google Scholar
• Birchfield , S. T. and S. Rangarajan . 2007 . Spatial histograms for region-based tracking . Electronics and Telecommunications Research Institute 29 ( 5 ): 697 – 699 .
   Google Scholar
• Comaniciu , D. , V. Ramesh , and P. Meer . 2003 . Kernel-based object tracking . IEEE Transactions on Pattern Analysis and Machine Intelligence 25 ( 5 ): 564 – 577 .
   Web of Science ®Google Scholar
• Cheng , H. and J. Hwang . 2009 . Adaptive particle sampling and adaptive appearance for multiple video object tracking . Signal Processing 89 ( 9 ): 1844 – 1849 .
   Google Scholar
• Gordon , N. J. , D. J. Salmond , and A. Smith . 1993 . Novel-approach to nonlinear non-Gaussian Bayesian state estimation . IEE Proceedings-F Radar and Signal Processing 140 ( 2 ): 107 – 113 .
   Web of Science ®Google Scholar
• Iosifidis , A. , Spyridon G. Mouroutsos , and Antonios Gasteratos . 2011 . A hybrid static/active video surveillance system . International Journal of Optomechatronics 5 ( 1 ): 80 – 95 .
   Web of Science ®Google Scholar
• Isard , M. and A. Blake . 1998 . CONDENSATION – conditional density propagation for visual tracking . International Journal of Computer Vision 29 ( 1 ): 5 – 28 .
   Web of Science ®Google Scholar
• Kristan , M. , J. Pers , S. Kovacic , and A. Leonardis . 2009 . A local-motion-based probabilistic model for visual tracking . Pattern Recognition 42 ( 9 ): 2160 – 2168 .
   Google Scholar
• Kwon , Junseok and Kyoung Mu Lee . 2010 . Visual tracking decomposition. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1269–1276 .
   Google Scholar
• Lazaros , N. , G. C. Sirakoulis , and A. Gasteratos . 2008 . Review of stereo vision algorithms: From software to hardware . International Journal of Optomechatronics 2 ( 4 ): 435 – 462 .
   Web of Science ®Google Scholar
• Leichter , I. , M. Lindenbaum , and E. Rivlin . 2010 . Mean shift tracking with multiple reference color histograms . Computer Vision and Image Understanding 114 ( 3 ): 400 – 408 .
   Google Scholar
• Ross , D. A. , J. Lim , R. S. Lin , and M. H. Yang . 2008 . Incremental learning for robust visual tracking . International Journal of Computer Vision 77 ( 1–3 ): 125 – 141 .
   Google Scholar
• Swain , M. and D. Ballard . 1991 . Color indexing . International Journal of Computer Vision 7 ( 1 ): 11 – 32 .
   Web of Science ®Google Scholar
• Ulges , Adrian , Christoph Lampert , and Daniel Keysers . 2006 . Spatiogram-based shot distances for video retrieval. Proceedings of TRECVID Workshop 1–10 .
   Google Scholar
• Wang , Fanglin , Shengyang Yu , and Jie Yanga . 2010 . Robust and efficient fragments-based tracking using mean shift . AEU-International Journal of Electronics and Communications 64 ( 7 ): 614 – 623 .
   Google Scholar
• Wang , H. Z. , D. Suter , K. Schindler , and C. H. Shen . 2007 . Adaptive object tracking based on an effective appearance filter . IEEE Transactions on Pattern Analysis and Machine Intelligence 29 ( 9 ): 1661 – 1667 .
   PubMedGoogle Scholar
• Wu , M. , X. Peng , Q. Zhang , and R. Zhao . 2010 . Patches-based Markov random field model for multiple object tracking under occlusion . Signal Processing 90 ( 5 ): 1518 – 1529 .
   Google Scholar
• Xie , N. , H. Ling , W. Hu , and X. Zhang . 2010. Use bin-ratio information for category and scene classification. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2313–2319.
   Google Scholar
• Yilmaz , A. , O. Javed , and M. Shah . 2006 . Object tracking: A survey . ACM Computing Surveys 38 ( 4 ): 45 .
   Google Scholar