iccv iccv2013 iccv2013-338 iccv2013-338-reference knowledge-graph by maker-knowledge-mining
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Author: Qinxun Bai, Zheng Wu, Stan Sclaroff, Margrit Betke, Camille Monnier
Abstract: We propose a randomized ensemble algorithm to model the time-varying appearance of an object for visual tracking. In contrast with previous online methods for updating classifier ensembles in tracking-by-detection, the weight vector that combines weak classifiers is treated as a random variable and the posterior distribution for the weight vector is estimated in a Bayesian manner. In essence, the weight vector is treated as a distribution that reflects the confidence among the weak classifiers used to construct and adapt the classifier ensemble. The resulting formulation models the time-varying discriminative ability among weak classifiers so that the ensembled strong classifier can adapt to the varying appearance, backgrounds, and occlusions. The formulation is tested in a tracking-by-detection implementation. Experiments on 28 challenging benchmark videos demonstrate that the proposed method can achieve results comparable to and often better than those of stateof-the-art approaches.
[1] A. Adam, E. Rivlin, and I. Shimshoni. Robust fragmentsbased tracking using the integral histogram. In CVPR, 2006. 2, 6, 7
[2] S. Avidan. Ensemble tracking. PAMI, 29, 2007. 1, 2
[3] B. Babenko, M.-H. Yang, and S. Belongie. Visual tracking with online multiple instance learning. CVPR, 2009. 1, 2, 5,
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17] 6, 7 L. Cehovin, M. Kristan, and A. Leonardis. An adaptive coupled-layer visual model for robust visual tracking. In ICCV, 2011. 2 R. T. Collins, Y. Liu, and M. Leordeanu. Online selection of discriminative tracking features. PAMI, 27, 2005. 2 N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005. 5 J. Fan, X. Shen, and Y. Wu. Scribble tracker: A mattingbased approach for robust tracking. PAMI, 34, 2012. 2 H. Grabner and H. Bischof. On-line boosting and vision. In CVPR, 2006. 1, 2, 6 K. Grauman. Matching Sets of Features for Efficient Retrieval and Recognition. PhD thesis, MIT, USA, 2006. 5 S. Hare, A. Saffari, and P. H. S. Torr. Struck: Structured output tracking with kernels. In ICCV, 2011. 6, 7, 8 Z. Kalal, K. Mikolajczyk, and J. Matas. Tracking-learningdetection. PAMI, 34(7), 2012. 5, 6, 7 J. Kwon and K. M. Lee. Visual tracking decomposition. In CVPR, 2010. 5, 7 T. P. Minka. Estimating a Dirichlet distribution. Technical report, Microsoft Research, 2003. 4 K. Ng, G. Tian, and M. Tang. Dirichlet and Related Distributions: Theory, Methods and Applications. Wiley Series in Probability and Statistics. John Wiley & Sons, 2011. 3 N. Oza. Online bagging and boosting. In IEEE Intl’ conf. on Systems, man and cybernetics, pages 2340–2345, 2005. 2, 6 F. Pernici. Facehugger: The ALIEN tracker applied to faces. In ECCV Workshops and Demonstrations. 2012. 2 A. Saffari, M. Godec, T. Pock, C. Leistner, and H. Bischof. Online multi-class LPBoost. In CVPR, 2010. 1, 2
[18] A. Saffari, C. Leistner, J. Santner, M. Godec, and H. Bischof. On-line random forests. In ICCV, 2009. 2
[19] J. Santner, C. Leistner, A. Saffari, T. Pock, and H. Bischof. Prost: Parallel robust online simple tracking. In CVPR, 2010. 5, 7
[20] L. Sevilla-Lara and E. G. Learned-Miller. Distribution fields for tracking. In CVPR, 2012. 1, 2, 5, 6, 7
[21] K. Zhang, L. Zhang, and M.-H. Yang. Real-time compressive tracking. In ECCV, 2012. 1, 2, 5, 7 2047