Abstract
Under dynamic and complex environment, the single feature methods usually can’t distinguish the target from background well, so that multiple features are considered in the paper. For each candidate, multiple features are extracted and conducted the sparse representation respectively, then observation probability is calculated by combinating reconstruction errors of multiple features in particle filter framework. Comparing with single feature method, the proposed method performed robust with better accuracy. And further experiments on some representative image sequences showed that the proposed method also performs well in complex scenarios, such as varying illumination, background clutter, and occlusion.
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References
Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Computing Surveys 38 (2006)
Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: ICCV (2009)
Kwak, S., Nam, W., Han, B., Han, J.H.: Learning Occlusion with Likelihoods for Visual Tracking. In: CVPR (2011)
Liu, B., Yang, L., Huang, J., Meer, P., Gong, L., Kulikowski, C.: Robust and fast collaborative tracking with two stage sparse optimization. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 624–637. Springer, Heidelberg (2010)
Wu, Y., Ling, H., Yu, J., Li, F., Mei, X., Cheng, E.: Blurred Target Tracking by Blur-driven Tracker. In: ICCV (2011)
Tseng, P.: On accelerated proximal gradient methods for convex–concave optimization. Technical report (2008), http://pages.cs.wisc.edu/~brecht/cs726docs/Tseng.APG.pdf
Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, New York (2001)
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Duan, X., Liu, J., Tang, X. (2013). Multi-cue Visual Tracking Based on Sparse Representation. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_54
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DOI: https://doi.org/10.1007/978-3-642-42057-3_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
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