Abstract
In this paper, a robust mean shift tracking algorithm based on refined appearance model (RAM) and online update strategy is proposed. The main idea of the proposed algorithm is to construct a more accurate appearance model to improve tracking precision and design an online update strategy to adjust to the appearance variation. At the beginning of the tracking, the simple mean shift tracking algorithm is applied on the first few frames to collect a set of target templates, which contains both foreground and background of the target. During the model construction, simple linear iterative clustering (SLIC) algorithm is exploited to obtain the superpixels of the target templates, and the superpixels are further clustered to distinguish the foreground from background. A weighted vector is then obtained based on the classified foreground from background, which is utilized to modify the kernel histogram appearance model. The following frames are processed based on the mean shift tracking algorithm with the modified appearance model, and the stable tracking results with no occlusion will be selected to update the appearance model. The concrete operation of model update is the same as model construction. Experiment results on some challenging test sequences indicate that the proposed algorithm can well cope with both appearance variation and background change to obtain a robust tracking performance. A further comprehensive experiment on OTB2013 demonstrates that the proposed tracking algorithm outperforms the state-of-the-art works in most cases.
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Achanta R, Shaji A, Smith K, Pascal F, Sabine S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2281
Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE Press, New York, pp 798–805
Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632
Birchfield S, Rangarajan S (2005) Spatiograms versus histograms for region-based tracking. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE Press, San Diego, pp 1158–1163
Bradski G (1998) Real time face and object tracking as a component of a perceptual user interface. In: Proceedings of the fourth IEEE workshop on applications of computer vision. IEEE Press, Princeton, pp 214–219
Cheng Y (1995) Mean shift, mode seeking and clustering. IEEE Trans Pattern Anal Mach Intell 17(8):790–799
Collins R (2003) Mean-shift blob tracking through scale space, IEEE Press, Wisconsin
Collins R, Liu Y, Leordeanu M (2005) Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell 27(10):1631–1643
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619
Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(2):564–577
Fukunaga F, Hostetler L (1975) The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 21(1):32–40
Grabner H, Grabner M, Bischof H (2006) Real-time tracking via on-line boosting. In: Proceedings of the British machine vision conference. Edinburgh, pp 47–56
Grabner H, Leistner C, Bischof H (2008) Semi-supervised on-line boosting for robust tracking. In: Proceedings of the European conference on computer vision. Springer-Verlag, Marseille, pp 234–247
Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE conference on computer vision and pattern recognition. IEEE Press, Providence, pp 1822–1829
Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422
Leichter I (2012) Mean shift trackers with cross-bin metrics. IEEE Trans Patt Anal Mach Intell 34(4):695–706
Matthews I, Ishikawa T, Baker S (2004) The template update problem. IEEE Trans Pattern Anal Mach Intell 26(6):810–815
Ning J, Zhang L, Zhang D, Wu C (2012) Robust mean-shift tracking with corrected background-weighted histogram. IET Comput Vis 6(1):62–69
Ning J, Zhang L, Zhang D, Wu C (2012) Scale and orientation adaptive mean shift tracking. IET Comput Vis 6(1):52–61
Perez P, Hue C, Vermaak J, Gangnet M (2002) Color-based probabilistic tracking. In: Proceedings of the European conference on computer vision. Springer-Verlag, Berlin, pp 661–675
Ross D, Lim J, Lin R, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1):125–141
Smeulders A, Chu D, Cucchiara R, Calderara S, Dehghan A, Shah M (2014) Visual tracking: an experimental survey. IEEE Trans Pattern Anal Mach Intell 36(7):1442–1468
Wang S, Lu H, Yang F, Yang M (2011) Superpixel tracking. In: Proceedings of the international conference on computer vision. IEEE Press, Barcelona, pp 1323–1330
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. IEEE Press, Portland, pp 2411–2418
Wu Y, Lim J, Yang MH (2015) Object tracking benchmark. Trans Pattern Anal Mach Intell 37(9):1834–1848
Zhang K, Zhang L, Yang M-H (2012) Real-time compressive tracking. In: Proceedings of the European conference on computer vision. Springer-Verlag, Berlin, pp 864–877
Zhuang B, Lu H, Xiao Z, Wang D (2014) Visual tracking via discriminative sparse similarity map. IEEE Trans Image Process 23(4):1872–1881
Zivkovic Z, Kröse B (2004) An EM-like algorithm for color-histogram based object tracking. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE Press, Washington, pp 798–803
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This research was supported by National Natural Science Foundation of China (No.61473309 and 61403414) and Natural Science Basic Research Plan in Shaanxi Province of China (No.2016JM6050).
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Yu, W., Hou, Z., Hu, D. et al. Robust mean shift tracking based on refined appearance model and online update. Multimed Tools Appl 76, 10973–10990 (2017). https://doi.org/10.1007/s11042-016-3472-5
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DOI: https://doi.org/10.1007/s11042-016-3472-5