Abstract
In this paper, we propose a novel object tracking method by fusing multiple features. The tracking task is formulated under Bayesian inference framework. The posterior probability is resolved by the sum of weighted likelihood observations. Graph based semi-supervised learning method is used for likelihood evaluation, and the distance between foreground and background histograms is used for weight estimation. We evaluate our tracking algorithm on some popular benchmark videos and achieve competitive results compared with some state-of-art algorithms.
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References
Jiang, N., Liu, W.-Y., Wu, Y.: Learning Adaptive Metric for Robust Visual Tracking. TIP 20(8), 2288–2300 (2011)
Babenko, B., Yang, M.H., Belongie, S.: Robust Object Tracking with Online Multiple Instance Learning. TPAMI 33(8), 1619–1632 (2011)
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragment-based tracking using the integral histogram. In: CVPR (2006)
Avidan, S.: Ensemble Tracking. TPAMI 29(2), 261–271 (2007)
Yin, Z., Porikli, F., Collins, R.: Likelihood Map Fusion for Visual Object Tracking. In: WACV (2008)
Zhu, X.: Semi-Supervised Learning Literature Survey. Technical Report, Department of Computer Sciences, University of Wisconsin, Madison (2005)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR (2005)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. TPAMI 24(7), 971–987 (2002)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, pp. 511–518 (2001)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with Local and Global Consistency. In: NIPS (2004)
Smola, A., Kondor, R.: Kernels and Regularization on Graphs. In: COLT (2003)
Ross, D., Kim, J., Lin, R.-S., Yang, M.-H.: Incremental Learning for Robust Visual Tracking. IJCV 77(1), 125–141 (2008)
Grabner, H., Grabner, M., Bischof, H.: Real-Time Tracking via On-Line Boosting. In: BMVC, pp. 47–56 (2006)
Grabner, H., Leistner, C., Bischof, H.: Semi-supervised On-Line Boosting for Robust Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)
Zelnik-Manor, L., Perona, P.: Self-Tuning Spectral Clustering. In: NIPS (2004)
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Zhou, Y., Rao, C., Lu, Q., Bai, X., Liu, W. (2012). Multiple Feature Fusion for Object Tracking. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_19
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DOI: https://doi.org/10.1007/978-3-642-31919-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
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