Abstract
It is a challenging task to track a shape variation object. In this paper, a novel discriminative metric learning based on multi-features appearance model is proposed for shape variation object tracking. Initially,we exploit the shape invariant properties and form multi-features appearance model, which consists of hue features, center-symmetric local binary pattern (CSLBP) at multiple scales, and orientation features. With the obtained multi-features appearance descriptor, we propose an improved bias discriminative component analysis (BDCA) classifier to distinguish the target object and background. In addition, a novel Mahalanobis distance metric is learned by BDCA classifier, which project the original space into a new space. Furthermore, based on the learned distance metric, the tracked object can be located in the new transformed feature space by matching the candidate image regions with templates in library. Compared with several other tracking algorithms, the experimental results demonstrate that the proposed algorithm is able to track an object accurately especially for object pose change, rotation and occlusion.
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References
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., van den Hengel, A.: A survey of appearance models in visual object tracking. ACM Transactions on Intelligent Systems and Technology, arXiv preprint arXiv:1303.4803 (2013)
Zhao, Q., Yang, Z., Tao, H.: Differential earth movers distance with its applications to visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 274–287 (2010)
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2006, pp. 798–805 (2006)
Zhou, H., Yuan, Y., Shi, C.: Object tracking using sift features and mean shift. Computer Vision and Image Understanding 113(3), 345–352 (2009)
Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust object tracking using joint color-texture histogram. International Journal of Pattern Recognition and Artificial Intelligence 23(7), 1245–1263 (2009)
Hager, G.D., Dewan, M., Stewart, C.V.: Multiple kernel tracking with ssd. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2004, pp. 790–797 (2004)
Elgammal, A., Duraiswami, R., Davis, L.: Probabilistic tracking in joint feature-spatial spaces. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2003, pp. 781–788 (2003)
Wang, X., Hua, G., Han, T.X.: Discriminative tracking by metric learning. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 200–214. Springer, Heidelberg (2010)
Jiang, N., Liu, W., Wu, Y.: Adaptive and discriminative metric differential tracking. In: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1161–1168 (2011)
Wu, Y.W., Ma, B.: Learning distance metric for contour tracking. Pattern Analysis Applications 17(2), 265–277 (2014)
Heikkila, M., Pietikainen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognition 42, 425–436 (2009)
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)
Hoi, S.C.H., Liu, W., Lyu, M.R., Ma, W.Y: Learning distance metrics with contextual constraints for image retrieval. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, pp. 2072–2078 (2006)
Godec, M., Roth, P.M., Bischof, H.: Hough-based tracking of non-rigid objects. In: 2011 IEEE International Conference on Computer Vision, pp. 81–88 (2011)
Yang, F., Lu, H., Chen, Y.-W.: Human tracking by multiple kernel boosting with locality affinity constraints. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part IV. LNCS, vol. 6495, pp. 39–50. Springer, Heidelberg (2011)
Zhong, W., Lu, H., Yang, M.: Robust object tracking via sparsity-based collaborative model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2012, pp. 1838–1845 (2012)
Yi, W., Lim, J., Yang, M.: Online object tracking: A benchmark. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2013, pp. 2411–2418 (2013)
Ross, D., Lim, J., Lin, R., Yang, M.: Incremental learning for robust visual tracking. International Journal of Computer Vision 77(1), 125–141 (2008)
Babenko, B., Yang, M., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)
Mei, X., Ling, H.: Robust visual tracking using L1 minimization. In: 2009 IEEE International Conference on Computer Vision, pp. 1436–1443 (2009)
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Zhao, L., Zhao, Q., Guo, W., Wang, Y. (2014). Discriminative Metric Learning for Shape Variation Object Tracking. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_26
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DOI: https://doi.org/10.1007/978-3-319-13560-1_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
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