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2018 | OriginalPaper | Chapter

Visual Tracking via Patch-Based Absorbing Markov Chain

Authors : Ziwei Xiong, Nan Zhao, Chenglong Li, Jin Tang

Published in: Structural, Syntactic, and Statistical Pattern Recognition

Publisher: Springer International Publishing

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Abstract

Bounding box description of target object usually includes background clutter, which easily degrades tracking performance. To handle this problem, we propose a general approach to learn robust object representation for visual tracking. It relies a novel patch-based absorbing Markov chain (AMC) algorithm. First, we represent object bounding box with a graph whose nodes are image patches, and introduce a weight for each patch that describes its reliability belonging to foreground object to mitigate background clutter. Second, we propose a simple yet effective AMC-based method to optimize reliable foreground patch seeds as their qualities are very important for patch weight computation. Third, based on the optimized seeds, we also utilize AMC to compute patch weights. Finally, the patch weights are incorporated into object feature description and tracking is carried out by adopting structured support vector machine algorithm. Experiments on the benchmark dataset demonstrate the effectiveness of our proposed approach.

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Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: IEEE Conference CVPR, pp. 2544–2550 (2010)

Chen, D., Yuan, Z., Hua, G., Wu, Y., Zheng, N.: Description-discrimination collaborative tracking. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 345–360. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_23CrossRef

Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. TPAMI 25, 564–577 (2003)CrossRef

Danelljan, M., Hager, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: Proceedings BMVC (2014)

Duffner, S., Garcia, C.: Pixeltrack: a fast adaptive algorithm for tracking non-rigid objects. In: Proceedings IEEE Conference ICCV (2013)

Hare, S., Saffari, A., Torr, P.H.S.: Struck: structured output tracking with kernels. In: Proceedings IEEE Conference ICCV (2011)

He, S., Yang, Q., Lau, R., Wang, J., Yang, M.H.: Visual tracking via locality sensitive histograms. In: Proceedings IEEE Conference CVPR (2013)

Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. TPAMI 37, 583–596 (2015)CrossRef

Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing markov chain. In: Proceedings IEEE Conference ICCV (2013)

10.

Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. TPAMI 34(7), 1409–1422 (2012)CrossRef

11.

Kim, H.U., Lee, D.Y., Sim, J.Y., Kim, C.S.: SOWP: spatially ordered and weighted patch descriptor for visual tracking. In: Proceedings IEEE Conference ICCV (2015)

12.

Li, C., Liang, X., Lu, Y., Zhao, N., Tang, J.: RGB-T object tracking: benchmark and baseline. arXiv:1805.08982 (2018)

13.

Li, C., Lin, L., Zuo, W., Tang, J.: Learning patch-based dynamic graph for visual tracking. In: Proceedings AAAI (2017)

14.

Li, C., Lin, L., Zuo, W., Tang, J., Yang, M.H.: Visual tracking via dynamic graph learning. arXiv:1710.01444 (2018)

15.

Li, C., Wu, X., Bao, Z., Tang, J.: ReGLe: spatially regularized graph learning for visual tracking. In: MM Proceedings ACM (2017)

16.

Li, X., Han, Z., Wang, L., Lu, H.: Visual tracking via random walks on graph model. IEEE Trans. Cybern. 46(9), 2144–2155 (2016)CrossRef

17.

Liu, T., Wang, G., Yang, Q.: Real-time part-based visual tracking via adaptive correlation filters. In: IEEE Conference CVPR (2015)

18.

Ma, C., Yang, X., Zhang, C., Yang, M.H.: Long-term correlation tracking. In: Proceedings IEEE Conference CVPR (2015)

19.

Tong, H., Faloutsos, C., Pan, J.Y.: Random walk with restart: fast solutions and applications. KAIS 14(3), 327–346 (2008)MATH

20.

Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. In: NIPS, pp. 809–817 (2013)

21.

Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. TPAMI 37, 1834–1848 (2015)CrossRef

22.

Yang, F., Lu, H., Yang, M.H.: Robust superpixel tracking. IEEE Trans. Image Process. 23(4), 1639–1651 (2014)MathSciNetCrossRef

23.

Yeo, D., Son, J., Han, B., Han, J.H.: Superpixel-based tracking-by-segmentation using markov chains. In: CVPR, pp. 511–520 (2017)

24.

Zhang, J., Ma, S., Sclaroff, S.: MEEM: robust tracking via multiple experts using entropy minimization. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 188–203. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_13CrossRef

25.

Zhang, K., Zhang, L., hsuan Yang, M.: Real-time compressive tracking. In: Proceedings ECCV (2012)

Title: Visual Tracking via Patch-Based Absorbing Markov Chain
Authors: Ziwei Xiong
Nan Zhao
Chenglong Li
Jin Tang
Publisher: Springer International Publishing
Book: Structural, Syntactic, and Statistical Pattern Recognition
Print ISBN: 978-3-319-97784-3

Electronic ISBN: 978-3-319-97785-0

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-97785-0_15

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