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Issue No.10 - October (2008 vol.30)
pp: 1814-1830
In this paper, we propose a multi-layered data association scheme with graph-theoretic formulation for tracking multiple objects that undergo switching dynamics in clutter. The proposed scheme takes as input object candidates detected in each frame. At the object candidate level, "tracklets'' are "grown'' from sets of candidates that have high probabilities of containing only true positives. At the tracklet level, a directed and weighted graph is constructed, where each node is a tracklet, and the edge weight between two nodes is defined according to the "compatibility'' of the two tracklets. The association problem is then formulated as an all-pairs shortest path (APSP) problem in this graph. Finally, at the path level, by analyzing the all-pairs shortest paths, all object trajectories are identified, and track initiation and track termination are automatically dealt with. By exploiting a special topological property of the graph, we have also developed a more efficient APSP algorithm than the general-purpose ones. The proposed data association scheme is applied to tennis sequences to track tennis balls. Experiments show that it works well on sequences where other data association methods perform poorly or fail completely.
Video analysis, Tracking, Graph Theory, Path and circuit problems
Fei Yan, William Christmas, Josef Kittler, "Layered Data Association Using Graph-Theoretic Formulation with Application to Tennis Ball Tracking in Monocular Sequences", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 10, pp. 1814-1830, October 2008, doi:10.1109/TPAMI.2007.70834
[1] P. Smith and G. Buechler, “A Branching Algorithm for Discriminating and Tracking Multiple Objects,” IEEE Trans. Automatic Control, pp. 101-104, 1975.
[2] Y. Bar-Shalom and T.E. Fortmanm, Tracking and Data Association. Academic Press, 1988.
[3] T. Quach and M. Farooq, “Maximum Likelihood Track Formation with the Viterbi Algorithm,” Proc. 33rd IEEE Conf. Decision and Control (CDC '94), pp. 271-276, 1994.
[4] R. Streit and T. Luginbuhl, “Probabilistic Multi-Hypothesis Tracking,” technical report, 1995.
[5] D. Reid, “An Algorithm for Tracking Multiple Targets,” IEEE Trans. Automatic Control, vol. 24, no. 6, pp. 843-854, 1979.
[6] Y. Bar-shalom, X. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation. John Wiley & Sons, 2001.
[7] P. Willett, Y. Ruan, and R. Streit, “The PMHT for Maneuvering Targets,” Proc. SPIE Conf. Signal and Data Processing of Small Targets '98, pp. 416-427, 1998.
[8] E. Mazor, A. Averbuch, Y. Bar-Shalom, and J. Dayan, “Interacting Multiple Model Methods in Target Tracking: A Survey,” IEEE Trans. Aerospace and Electronic Systems, vol. 34, no. 1, pp. 103-122, 1998.
[9] D. Musicki, B.F.L. Scala, and R.J. Evans, “Integrated Track Splitting Filter for Maneuvering Targets,” Proc. Seventh IEEE Int'l Conf. Information Fusion (FUSION '04), 2004.
[10] S.S. Blackman, M.T. Busch, and R.F. Popoli, “IMM-MHT Tracking and Data Association for Benchmark Tracking Problem,” Proc. 14th Am. Control Conf. (ACC '95), vol. 4, pp. 2606-2610, 1995.
[11] N. Owens, C. Harris, and C. Stennett, “Hawk-Eye Tennis System,” Proc. Int'l Conf. Visual Information Eng. (VIE '03), 2003.
[12] G.S. Pingali, Y. Jean, and I. Carlbom, “Real Time Tracking for Enhanced Tennis Broadcasts,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition (CVPR '98), pp. 260-265, 1998.
[13] X. Yu, C. Sim, J.R. Wang, and L. Cheong, “A Trajectory-Based Ball Detection and Tracking Algorithm in Broadcast Tennis Video,” Proc. Int'l Conf. Image Processing (ICIP '04), vol. 2, pp. 1049-1052, 2004.
[14] H. Miyamori and S. Iisaku, “Video Annotation for Content-Based Retrieval Using Human Behavior Analysis and Domain Knowledge,” Proc. Fourth Int'l Conf. Automatic Face and Gesture Recognition (AFGR '00), pp. 320-325, 2000.
[15] V. Lepetit, A. Shahrokni, and P. Fua, “Robust Data Association for Online Applications,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition (CVPR '03), vol. 1, pp. 281-288, 2003.
[16] M.A. Fischler and R.C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Comm. ACM, vol. 24, no. 6, pp.381-395, 1981.
[17] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, second ed. Cambridge Univ. Press, 2004.
[18] D.R. Myatt, P.H.S. Torr, S.J. Nasuto, J.M. Bishop, and R. Craddock, “Napsac: High Noise, High Dimensional Robust Estimation—It's in the Bag,” Proc. 13th British Machine Vision Conf. (BMVC '02), pp.458-467, 2002.
[19] O. Chum, J. Matas, and J. Kittler, “Locally Optimized RANSAC,” Proc. 25th DAGM Pattern Recognition Symp., pp. 236-243, 2003.
[20] P.H.S. Torr and A. Zisserman, “MLESAC: A New Robust Estimator with Application to Estimating Image Geometry,” Computer Vision and Image Understanding, vol. 78, pp. 138-156, 2000.
[21] B.J. Tordoff and D.W. Murray, “Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1523-1535, Oct. 2005.
[22] E.W. Dijkstra, “A Note on Two Problems in Connexion with Graphs,” Numerische Mathematik, vol. 1, pp. 269-271, 1959.
[23] V. Mottl, A. Kostin, and I. Muchnik, “Generalized Edge-Preserving Smoothing for Signal Analysis,” Proc. IEEE Workshop Nonlinear Signal and Image Analysis, 1997.
[24] F. Yan, A. Kostin, W. Christmas, and J. Kittler, “A Novel Data Association Algorithm for Object Tracking in Clutter with Application to Tennis Video Analysis,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition (CVPR '06), 2006.
[25] B.A. Davey and H.A. Priestley, Introduction to Lattices and Order. Cambridge Univ. Press, 2002.
[26] R.W. Floyd, “Algorithm 97: Shortest Path,” Comm. ACM, vol. 5, no. 6, p. 345, 1962.
[27] D.B. Johnson, “Efficient Algorithms for Shortest Paths in Sparse Networks,” J. ACM, vol. 24, no. 1, pp. 1-13, 1977.
[28] T.H. Cormen, C.E. Leiserson, and R.L. Rivest, Introduction to Algorithms. MIT Press, 1990.
[29] O. Chum, J. Matas, and S. Obdrzalek, “Enhancing RANSAC by Generalized Model Optimization,” Proc. Sixth Asian Conf. Computer Vision (ACCV '04), vol. 2, pp. 812-817, 2004.
[30] J.K. Wolf, A.M. Viterbi, and S.G. Dixon, “Finding the Best Set of kPaths through a Trellis with Application to Multitarget Tracking,” IEEE Trans. Aerospace and Electronic Systems, vol. 25, pp. 287-296, 1989.
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