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.
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References
Cannons K. A Review of Visual Tracking. Technical Report CSE-2008-07. 2008
Yilmaz A, Javed O, Shah M. Object tracking: a survey. ACM Comput Surv, 2006, 38: 1–35
Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. Pattern Anal Mach Intell, 2003, 25: 564–577
Ross D, Lim J, Lin R-S, et al. Incremental learning for robust visual tracking. Int J Comput Vision, 2008, 77: 125–141
Mei X, Ling H. Robust visual tracking using l1 minimization. In: Proceedings of IEEE International Conference on Computer Vision, Nice, 2009. 1436–1443
Fan J, Shen X, Wu Y. Scribble tracker: a matting-based approach for robust tracking. Pattern Anal Mach Intell, 2012, 34: 1633–1644
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
Kwon J, Lee K M. Visual tracking decomposition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010. 1269–1276
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
Mei X, Ling H. Robust visual tracking and vehicle classification via sparse rep-resentation. Pattern Anal Mach Intell, 2011, 33: 2259–2272
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
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
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
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
Collins R, Liu Y, Leordeanu M. Online selection of discriminativetracking features. Pattern Anal Mach Intell, 2005, 27: 1631–1643
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
Babenko B, Yang M-H, Belongie S. Robust object tracking with online multiple instance learning. Pattern Anal Mach Intell, 2011, 33: 1619–1632
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
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
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
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
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
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
Xu C, Tao D C, Xu C. Multi-view intact space learning. IEEE Trans Pattern Anal Mach Intell, 2015, 37: 2531–2544
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
Zhang K, Zhang L, Yang M-H. Real-time compressive tracking. In: Proceedings of European Conference on Computer Vision, Florence, 2012. 864–877
Avidan S. Support vector tracking. IEEE Trans Pattern Anal Mach Intell, 2004, 26: 1064–1072
Collins R, Liu Y, Leordeanu M. Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell, 2005, 27: 1631–1643
Avidan S. Ensemble tracking. IEEE Trans Pattern Anal Mach Intell, 2007, 29: 261–271
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
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
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
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
Grabner H, Grabner M, Bischof H. Real-time tracking via online boosting. In: Proceedings of British Machine Vision Conference, Edinburgh, 2006. 47–56
Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell, 2011, 34: 1409–1422
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
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Sun, H., Li, J., Chang, J. et al. Efficient compressive sensing tracking via mixed classifier decision. Sci. China Inf. Sci. 59, 072102 (2016). https://doi.org/10.1007/s11432-015-5424-5
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DOI: https://doi.org/10.1007/s11432-015-5424-5