Abstract
Color histograms are widely used for visual tracking due to their robustness against object deformations. However, traditional histogram representation often suffers from problems of partial occlusion, background cluttering and other appearance corruptions. In this paper, we propose a probabilistic index histogram to improve the discriminative power of the histogram representation. With this modeling, an input frame is translated into an index map whose entries indicate indexes to a separate bin. Based on the index map, we introduce spatial information and the bin-ratio dissimilarity in histogram comparison. The proposed probabilistic indexing technique, together with the two robust measurements, greatly increases the discriminative power of the histogram representation. Both qualitative and quantitative evaluations show the robustness of the proposed approach against partial occlusion, noisy and clutter background.
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References
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking (2003)
Black, M., Jepson, A.: Eigentracking: Robust matching and tracking of articulated objects using view-based representation. In: Proc. ICCV, pp. 329–342 (1995)
Jepson, A., Fleet, D., El-Maraghi, T.: Robust online appearance models for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1296–1311 (2003)
Black, M., Fleet, D., Yacoob, Y.: A framework for modeling appearance change in image sequence. In: Proc. ICCV, pp. 660–667 (1998)
Zhou, S., Chellappa, R., Moghaddam, B.: Visual tracking and recongnition using appearance-adaptive models in particles filters. IEEE Transaction on Image Processing 13, 1491–1506 (2004)
Lim, J., Ross, D., Lin, R., Yang, M.: Incremental learning for visual tracking. In: NIPS, pp. 793–800 (2005)
Chen, S., Li, Y., Guan, Q., Xiao, G.: Real-time three-dimensional surface measurement by color encoded light projection. Applied Physics Letters 89, 111108 (2009)
Jojic, N., Caspi, Y.: Capturing image structure with probabilistic index maps (2004)
Xie, N., Ling, H., Hu, W., Zhang, Z.: Use bin-ratio information for category and scene classification (2010)
Georgescu, B., Meer, P.: Point matching under large image deformations and illumination changes. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 674–689 (2004)
Georgescu, B., Meer, P.: Spatiograms vs. histograms for region based tracking. In: IEEE Conf. on Computer Vision and Pattern Recognition (2005)
Zhao, Q., Brennan, S., Tao, H.: Differential emd tracking. In: Proc. ICCV (2007)
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragmentsbased tracking using the integral histogram. In: IEEE Conf. Computer Vision and Pattern Recognition (2006)
Jordan, M., Ghahramani, Z., Jaakkola, T., Saul, L.: An introduction to variational methods for graphical models. In: Jordan, M.I. (ed.) Learning in Graphical Models. Kluwer Academic Publishers, Dordrecht (1998)
Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 343–356. Springer, Heidelberg (1996)
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Li, W., Zhang, X., Xie, N., Hu, W., Luo, W., Ling, H. (2011). Probabilistic Index Histogram for Robust Object Tracking. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6468. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22822-3_19
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DOI: https://doi.org/10.1007/978-3-642-22822-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22821-6
Online ISBN: 978-3-642-22822-3
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