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SCIENCE CHINA Information Sciences, Volume 59, Issue 7: 072102(2016) https://doi.org/10.1007/s11432-015-5424-5

Efficient compressive sensing tracking via mixed classifier decision

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  • ReceivedOct 12, 2015
  • AcceptedDec 29, 2015
  • PublishedJun 16, 2016

Abstract

Recent years have witnessed successful use of tracking-by-detection methods, with a number of promising results being achieved. Most of these algorithms use a sliding window to collect samples and then employ these samples to train and update the classifiers. They also use an updated classifier to establish the appearance model and they take the maximum response value of the classifier as the location of the target within a fixed radius. Compressive Tracking (CT) is a novel tracking-by-detection algorithm that updates the appearance model in a compressed domain. However, the conventional CT algorithm uses a single classifier to detect the target, and if the selected region drifts, the classifier may become inaccurate. Furthermore, the CT algorithm updates the classifier parameters with a constant learning rate. Therefore, if the target is completely occluded for an extended period, the classifier will instead learn the features of the covered object and the target will ultimately be lost. To overcome these problems, we present a compressive sensing tracking algorithm using mixed classifier decision. The main improvements in our algorithm are that it adopts mixed classifiers to locate the target and it applies a dynamic learning rate to update the appearance model. An experimental comparison with state-of-the-art algorithms on eight benchmark video sequences in complicated situations shows that the proposed algorithm achieves the best performance with 12 pixels on the average center location error and 66.82\% on the average overlap score.


Acknowledgment

Acknowledgments

This work was supported in part by National Basic Research Program of China (973 Program) (Grant No. 2012CB719905) and National Natural Science Foundation of China (Grant No. 61471274).


References

[1] Cannons K. A Review of Visual Tracking. Technical Report CSE-2008-07. 2008. Google Scholar

[2] Yilmaz A, Javed O, Shah M. Object tracking: a survey. ACM Comput Surv, 2006, 38: 1-35 CrossRef Google Scholar

[3] Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. Pattern Anal Mach Intell, 2003, 25: 564-577 CrossRef Google Scholar

[4] Ross D, Lim J, Lin R-S, et al. Incremental learning for robust visual tracking. Int J Comput Vision, 2008, 77: 125-141 CrossRef Google Scholar

[5] Mei X, Ling H. Robust visual tracking using l1 minimization. In: Proceedings of IEEE International Conference on Computer Vision, Nice, 2009. 1436--1443. Google Scholar

[6] Fan J, Shen X, Wu Y. Scribble tracker: a matting-based approach for robust tracking. Pattern Anal Mach Intell, 2012, 34: 1633-1644 CrossRef Google Scholar

[7] Wu Y, Huang T S. Robust visual tracking by integrating multiple cues based on co-inference learning. Int J Comput Vision, 2004, 58: 55-71 CrossRef Google Scholar

[8] Kwon J, Lee K M. Visual tracking decomposition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010. 1269--1276. Google Scholar

[9] Adam A, Rivlin E, Shimshoni I. Robust fragments-based tracking using the integral histogram. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, New York, 2006. 798--805. Google Scholar

[10] Mei X, Ling H. Robust visual tracking and vehicle classification via sparse rep-resentation. Pattern Anal Mach Intell, 2011, 33: 2259-2272 CrossRef Google Scholar

[11] Li H, Shen C, Shi Q. Real-time visual tracking using compressive sensing. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2011. 1305--1312. Google Scholar

[12] Liu B Y, Huang J Z, Yang L, et al. Robust tracking using local sparse appearance model and k-selection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2011. 1313--1320. Google Scholar

[13] Jia X, Lu H, Yang M-H. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2012. 1822--1829. Google Scholar

[14] Zhang T, Ghanem B, Liu S, et al. Robust visual tracking via structured multi-task sparse learning. Int J Comput Vision, 2013, 101: 367-383 CrossRef Google Scholar

[15] Collins R, Liu Y, Leordeanu M. Online selection of discriminativetracking features. Pattern Anal Mach Intell, 2005, 27: 1631-1643 CrossRef Google Scholar

[16] Babenko B, Yang M-H, Belongie S. Visual tracking with online multiple instance learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009. 983--990. Google Scholar

[17] Babenko B, Yang M-H, Belongie S. Robust object tracking with online multiple instance learning. Pattern Anal Mach Intell, 2011, 33: 1619-1632 CrossRef Google Scholar

[18] Kalal Z, Matas J, Mikolajczyk K. P-N learning: bootstrapping binary classifier by structural constraints. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010. 49--56. Google Scholar

[19] Zhang Y, Du B, Zhang L. A sparse Representation-Based binary hypothesis model for target detection in hyperspectral images. IEEE Trans Geosci Remote Sens, 2015, 53: 1346-1354 CrossRef Google Scholar

[20] Tao D, Cheng J, Song M, et al. Manifold ranking-based matrix factorization for saliency detection. IEEE Trans Neural Netw Lear Syst, in press. doi: 10.1109/TNNLS.2015.2461554. Google Scholar

[21] Tao D, Lin X, Jin L, et al. Principal component 2-dimensional long short-term memory for font recognition on single Chinese characters. IEEE Trans Cybernetics, in press. doi: 10.1109/TCYB.2015.2414920. Google Scholar

[22] Tao D C, Tang X O, Li X L, et al. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell, 2006, 28: 1088-1099 CrossRef Google Scholar

[23] Tao D C, Li X L, Wu X D, et al. General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell, 2007, 29: 1700-1715 CrossRef Google Scholar

[24] Xu C, Tao D C, Xu C. Multi-view intact space learning. IEEE Trans Pattern Anal Mach Intell, 2015, 37: 2531-2544 CrossRef Google Scholar

[25] Liu T L, Tao D C. Classification with noisy labels by importance reweighting. IEEE Trans Pattern Anal Mach Intell, in press. doi: 10.1109/TPAMI.2015.2456899. Google Scholar

[26] Zhang K, Zhang L, Yang M-H. Real-time compressive tracking. In: Proceedings of European Conference on Computer Vision, Florence, 2012. 864--877. Google Scholar

[27] Avidan S. Support vector tracking. IEEE Trans Pattern Anal Mach Intell, 2004, 26: 1064-1072 CrossRef Google Scholar

[28] Collins R, Liu Y, Leordeanu M. Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell, 2005, 27: 1631-1643 CrossRef Google Scholar

[29] Avidan S. Ensemble tracking. IEEE Trans Pattern Anal Mach Intell, 2007, 29: 261-271 CrossRef Google Scholar

[30] Grabner H, Leistner C, Bischof H. Semi-supervised on-line boosting for robust tracking. In: Proceedings of European Conference on Computer Vision, Prague, 2008. 234--247. Google Scholar

[31] Zhou Q, Lu H, Yang M H. Online multiple support instance tracking. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, Ljubljana, 2011. 545--552. Google Scholar

[32] Hare S, Saffari A, Torr P. Struck: structured output tracking with kernels. In: Proceedings of IEEE International Conference on Computer Vision, Barcelona, 2011. 263--270. Google Scholar

[33] Henriques F, Caseiro R, Martins P, et al. Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of European Conference on Computer Vision, Florence, 2012. 702--715. Google Scholar

[34] Grabner H, Grabner M, Bischof H. Real-time tracking via online boosting. In: Proceedings of British Machine Vision Conference, Edinburgh, 2006. 47--56. Google Scholar

[35] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell, 2011, 34: 1409-1422 Google Scholar

[36] Wu Y, Lim J, Yang M H. Online object tracking: a benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, 2013. 2411--2418. Google Scholar

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