S. Saravanakumar
C. G. Saneem Ahmed
ABSTRACT
Multiple object tracking is one of the most advanced researches in the field of computer vision. Various algorithms and approaches have been proposed and most of them use both foreground and background information. In this paper, we propose a multiple object strategy along with a failure recovery mechanism. The frames are modeled as target and non-target and based on which the tracking strategy works. The pixel values in the target and non-target group is transformed to HSV color space and represented as true and gray value. While tracking multiple objects, the tracking fails during occlusion, we handle this situation by using a suitable failure detection strategy. The histograms of the target objects in the current frame and the next frame is used for failure recovery. For evaluating the performance of the proposed method, we have used PETS video sequences and compare it with some of the recently proposed method. The result is encouraging.
- J. P. MacCormick and A. Blake, "A probabilistic exclusion principle for tracking multiple objects," in ICCV99, 1999, pp. 572--578.Google Scholar
- H. Tao, H. S. Sawhney, and R. Kumar, "A sampling algorithm for tracking multiple objects," in Vision Algorithms 99, 1999. Google ScholarDigital Library
- M. Isard and J. P. MacCormick, "Bramble: A bayesianGoogle Scholar
- C. Hue, J. P. Le Cadre, and P. Perez, "Tracking multiple objects with particle_ltering," IEEE Trans. on Aerospace and Electronic Systems, vol. 38, no. 3, pp. 791--812, July 2002.Google ScholarCross Ref
- D. B. Reid, "An algorithm for tracking multiple targets," AC, vol. 24, no. 6, pp. 843--854, December 1979.Google Scholar
- T. E. Fortmann, Y. Bar-Shalom, and M. Sche_e, "Sonar tracking of multiple targets using joint probabilistic data association," IEEE Journal Oceanic Eng., vol. OE-8, pp. 173--184, July 1983.Google ScholarCross Ref
- R. L. Streit and T. E. Luginbuhl, "Maximum likelihood method for probabilistic multi-hypothesis tracking," in Proceedings of SPIE International Symposium, Signal and Data Processing of Small Targets, 1994.Google Scholar
- H. Gauvrit and J. P. Le Cadre, "A formulation of multitarget tracking as an incomplete data problem," IEEE Trans. on multiple-blob tracker," in ICCV01, 2001, pp. II: 34--41. Aerospace and Electronic Systems, vol. 33, no. 4, pp. 1242--1257, Oct 1997.Google Scholar
- A. Vadivel, M. Mohan, Sural Shamik, A. K. Majumdar, "Object Level Frame Comparison for Video Shot Detection", IEEE Workshop on Motion Video Computing (WACV/MOTIONS '05), vol. 2, pp. 235--240, 2005. Google ScholarDigital Library
- C. Hua, H. Wu, Q. Chen and T. Wada, "K- means Tracker: A General Algorithm for Tracking People", Journal of Multimedia, Vol. 1, No. 4, 2006.Google ScholarCross Ref
- Video Dataset: http://www.cvg.rdg.ac.uk/PETS2009/a.htmlGoogle Scholar
- Shiuh-Ku Weng, Chung Ming Kuo and Shu-Kang Tu, "Video object tracking using adaptive Kalman filter", Journal of Visual Communication and Image Representation, Volume 17, Issue 6, pp. 1190--1208, 2006. Google ScholarDigital Library
Index Terms
- Multiple object tracking using HSV color space
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