Abstract
Recently a category of tracking methods based on “tracking-by-detection” is widely used in visual tracking problem. Most of these methods update the classifier online using the samples generated by the tracker to handle the appearance changes. However, the self-updating scheme makes these methods suffer from drifting problem because of the incorrect labels of weak classifiers in training samples. In this paper, we split the class labels into true labels and noise labels and model them by sparse representation. A novel dynamic classifier selection method, robust to noisy training data, is proposed. Moreover, we apply the proposed classifier selection algorithm to visual tracking by integrating a part based online boosting framework. We have evaluated our proposed method on 12 challenging sequences involving severe occlusions, significant illumination changes and large pose variations. Both the qualitative and quantitative evaluations demonstrate that our approach tracks objects accurately and robustly and outperforms state-of-the-art trackers.
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References
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR, vol. (1), pp. 798–805 (2006)
Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29, 261–271 (2007)
Bai, T., Li, Y.F.: Robust visual tracking with structured sparse representation appearance model. Pattern Recognition 45, 2390–2404 (2012)
Babenko, B., Yang, M.H., Belongie, S.J.: Visual tracking with online multiple instance learning. In: CVPR, pp. 983–990 (2009)
Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust ℓ1 tracker using accelerated proximal gradient approach. In: CVPR (2012)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25, 564–575 (2003)
Everingham, M., Gool, L.J.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88, 303–338 (2010)
Grabner, H., Bischof, H.: On-line boosting and vision. In: CVPR, vol. (1), pp. 260–267 (2006)
Grabner, H., Leistner, C., Bischof, H.: Semi-supervised On-Line Boosting for Robust Tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)
Gong, H., Sim, J., Likhachev, M., Shi, J.: Multi-hypothesis motion planning for visual object tracking. In: ICCV, pp. 619–626 (2011)
Hare, S., Saffari, A., Torr, P.H.S.: Struck: Structured output tracking with kernels. In: ICCV, pp. 263–270 (2011)
Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR (2012)
Kalal, Z., Matas, J., Mikolajczyk, K.: P-n learning: Bootstrapping binary classifiers by structural constraints. In: CVPR, pp. 49–56 (2010)
Ko, A.H.R., Sabourin, R., de Souza Britto Jr., A.: From dynamic classifier selection to dynamic ensemble selection. Pattern Recognition 41, 1718–1731 (2008)
Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR, pp. 1269–1276 (2010)
Kwon, J., Lee, K.M.: Tracking by sampling trackers. In: ICCV, pp. 1195–1202 (2011)
Li, G., Qin, L., Huang, Q., Pang, J., Jiang, S.: Treat samples differently: Object tracking with semi-supervised online covboost. In: ICCV, pp. 627–634 (2011)
Li, X., Shen, C., Shi, Q., Dick, A., van den Hengel, A.: Non-sparse linear representations for visual tracking with online reservoir metric learning. In: CVPR (2012)
Liu, B., Huang, J., Yang, L., Kulikowski, C.A.: Robust tracking using local sparse appearance model and k-selection. In: CVPR, pp. 1313–1320 (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)
Mei, X., Ling, H.: Robust visual tracking using ℓ1 minimization. In: ICCV, pp. 1436–1443 (2009)
Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. International Journal of Computer Vision 77, 125–141 (2008)
Stalder, S., Grabner, H., van Gool, L.: Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition. In: ICCV Workshops, pp. 1409–1416 (2009)
Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. (1), pp. 511–518 (2001)
Viola, P.A., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. In: NIPS (2005)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)
Wu, Y., Ling, H., Yu, J., Li, F., Mei, X., Cheng, E.: Blurred target tracking by blur-driven tracker. In: ICCV, pp. 1100–1107 (2011)
Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: CVPR (2012)
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Chen, Y., Wang, Q. (2013). Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_3
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DOI: https://doi.org/10.1007/978-3-642-37431-9_3
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