Abstract
In this work, we propose a tracking algorithm that robustly handles complex variations in target appearance, scale, occlusion, and background. In particular, the algorithm exploits a novel superpixel-based appearance model for visual tracking. From the initial tracking window, we extract superpixels and compute their histogram features. In subsequent frames, we search for the region that maximizes the similarity of the superpixel features. Our algorithm detects target occlusion and updates the appearance model accordingly. As well, the model is updated to handle large-scale variations. We present experimental results on several publicly available challenging sequences. Qualitative and quantitative evaluation of our tracking algorithm show improved performance over 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: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 798–805 (2006)
Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. International Journal of Computer Vision 77(1-3), 125–141 (2008)
Kwon, J., Lee, K.M.: Visual tracking decomposition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1269–1276 (2010)
Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: Prost: Parallel robust online simple tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 723–730 (2010)
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)
Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(8), 1619–1632 (2011)
Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: Proceedings of the International Conference on Computer Vision, pp. 1436–1443 (2009)
Yang, M., Yuan, J., Wu, Y.: Spatial selection for attentional visual tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Veksler, O., Boykov, Y., Mehrani, P.: Superpixels and supervoxels in an energy optimization framework. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 211–224. Springer, Heidelberg (2010)
Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K.: Turbopixels: Fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(12), 2290–2297 (2009)
Moore, A.P., Prince, S.J.D., Warrell, J., Mohammed, U., Jones, G.: Superpixel lattices. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Wang, S., Lu, H., Yang, F., Yang, M.H.: Superpixel tracking. In: Proceedings of the International Conference on Computer Vision, pp. 1–8 (2011)
Swain, M.J., Ballard, D.H.: Color indexing. International Journal of Computer Vision 7, 11–32 (1991)
Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: Proceedings of the British Machine Vision Conference, pp. 47–56 (2006)
Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forests. In: IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1393–1400 (2009)
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Nejhum, S., Rushdi, M., Ho, J. (2013). Visual Tracking Using Superpixel-Based Appearance Model. In: Chen, M., Leibe, B., Neumann, B. (eds) Computer Vision Systems. ICVS 2013. Lecture Notes in Computer Science, vol 7963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39402-7_22
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DOI: https://doi.org/10.1007/978-3-642-39402-7_22
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