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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 11/2019

13-09-2019 | Original Article

Robust visual tracking using discriminative sparse collaborative map

Authors: Zhenghua Zhou, Weidong Zhang, Jianwei Zhao

Published in: International Journal of Machine Learning and Cybernetics | Issue 11/2019

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Abstract

Visual tracking is a challenging task as it needs to consider the appearance variations due to some intrinsic and extrinsic interference factors in the process of tracking. This paper proposes a robust visual tracking algorithm based on the discriminative sparse collaborative (DSC) map and the alternating direction method of multipliers (ADMM). In the proposed visual tracker, named DSC tracker, a novel multi-task reverse sparse representation model based on the group sparse representation and group collaborative representation is proposed. Different from the traditional trackers that use the accelerated proximal gradient for solution, an effective method called ADMM is adopted to solve the proposed optimization model. With the solution, we can construct the discriminative features that contain the sparsity and coordination for the candidates on all templates simultaneously. Many comparison experiments illustrate that the proposed DSC tracker outperforms the DSS tracker as well as several state-of-the-art trackers.
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Literature
1.
Liu TS, Kong J, Jiang M, Liu CH, Gu XF, Wang XF (2019) Collaborative model with adaptive selection scheme for visual tracking. Int J Mach Learn Cybern 10(2):215–228CrossRef
2.
Zhuang B, Lu H, Xiao Z, Wang D (2014) Visual tracking via discriminative sparse similarity map. IEEE Trans Image Process 23(4):1872–1881MathSciNetCrossRef
3.
Collins RT, Liu Y, Leordeanu M (2005) Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell 27(10):1631–1643CrossRef
4.
Avidan S (2007) Ensemble tracking. IEEE Trans Pattern Anal Mach Intell 29(2):261–271CrossRef
5.
Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632CrossRef
6.
Liu TS, Kong J, Jiang M, Liu CH, Gu XF, Wang XF (2019) Collaborative model with adaptive selection scheme for visual tracking. Int J Mach Learn Cybern 10(2):215–228CrossRef
7.
Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–575CrossRef
8.
Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 1. pp 798–805
9.
Ross DA, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141CrossRef
10.
11.
Mei X, Ling H (2009) Robust visual tracking using \(\ell _1\) minimization. In: 2009 IEEE 12th International Conference on Computer Vision, pp 1436–1443
12.
Bao C, Wu Y, Ling H, Ji H (2012) Real time robust \(L_1\) tracker using accelerated proximal gradient approach. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 1830–1837
13.
Wang D, Lu H, Yang MH (2013) Online object tracking with sparse prototypes. IEEE Trans Image Process 22(1):314–325MathSciNetCrossRef
14.
Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 1822–1829
15.
Zhang TZ, Ghanem B, Liu S, Ahuja N (2012) Robust visual tracking via multi-task sparse learning. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 2042–2049
16.
Zhang TZ, Ghanem B, Liu S, Ahuja N (2013) Robust visual tracking via dtructured multi-task sparse learning. Int J Comput Vis 101:367–383MathSciNetCrossRef
17.
Wu GX, Zhao CX, Lu WJ, Xu W (2015) Efficient structured \(\ell _1\) tracker based on laplacian error distribution. Int J Mach Learn Cybern 6(4):581–591CrossRef
18.
Zhang T, Liu S, Xu C, Yan S, Ghanem B, Ahuja N (2015) Structural sparse tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 7–12. pp 150–158
19.
Wang D, Lu H, Xiao Z, Yang MH (2015) Inverse sparse tracker with a locally weighted distance metric. IEEE Trans Image Process 24(9):2646–2657MathSciNetCrossRef
20.
Chartrand R, Wohlberg B (2013) A nonconvex ADMM algorithm for group sparsity with sparse groups. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp 6009–6013
21.
Zhao ZQ, Ping F, Guo JJ, Yuan CH, Wu B (2018) A hybrid tracking framework based on kernel correlation filtering and particle filtering. Neurocomputing 297(5):40–49CrossRef
22.
Liu L, Xi ZH, Sun Q (2019) Multi-vision tracking and collaboration based on spatial particle filter. J Vis Commun Image Represent 59:316–326CrossRef
23.
Qian XY, Han L, Wang YD, Ding M (2018) Deep learning assisted robust visual tracking with adaptive particle filtering. Signal Process Image Commun 60:183–192CrossRef
24.
Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 2411–2418
25.
Kwon J, Lee KM (2010) Visual tracking decomposition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 1269–1276
26.
Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 7575. PART 4, pp 702–715CrossRef
27.
Henriques JF, Rui C, Martins P, Batista J (2014) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596CrossRef
28.
Zhang K, Liu Q, Wu Y, Yang MH (2016) Robust visual tracking via convolutional networks without training. IEEE Trans Image Process 25(4):1779–1792MathSciNetMATH
Metadata
Title
Robust visual tracking using discriminative sparse collaborative map
Authors
Zhenghua Zhou
Weidong Zhang
Jianwei Zhao
Publication date
13-09-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 11/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-01011-7

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