Abstract
Object tracking is a challenging task especially when occlusion occurs. In this paper, we propose a robust tracking method via structure learning and patch refinement to handle occlusion problem. First, we pose the tracking task as a structured output learning problem to mitigate the gap between pattern classification and the objective of object tracking. Contrary to the random target candidates selection method, we utilize the object independent proposal strategy to generate high quality training and testing samples in structured learning. Second, we over-segment the tracked target to a set of superpixel patches, and then train a background/foreground binary classifier to remove the background patches within the tracked object rectangle area for refining the tracking precision. The objective of target refining is to mitigate tracking model degradation and enhance model robustness for adapting our tracker for long-term and accurate tracking. Experimental results conducted on publicly available tracking dataset demonstrate that the proposed tracking method achieves excellent performance in handling target occlusion.
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References
Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vision 77(1), 125–141 (2008)
Mei, X., Ling, H.: Robust visual tracking using L1 minimization. In: IEEE International Conference on Computer Vision, pp. 1436–1443. IEEE, Kyoto (2009)
Zhong, W., Lu, H., Yang, M.: Robust object tracking via sparse collaborative appearance model. IEEE Trans. Image Process. 23(5), 2356–2368 (2004)
Avidan, S.: Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1064–1072 (2004)
Hare, S., Golodetz, S., Saffari, A., et al.: Struck: Structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2016)
Bolme, D.S., Beveridge, J.R., Draper, B., Lui, Y.M., et al.: Visual object tracking using adaptive correlation filters. In: IEEE International Conference on Computer Vision, pp. 2544–2550. IEEE, Istanbul (2010)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)
Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: IEEE Computer vision and pattern recognition, pp. 1822–1829. IEEE, Providence (2012)
Zhong, W., Lu, H., Yang, M.: Robust object tracking via sparse collaborative appearance model. IEEE Trans. Image Process. 23(5), 2356–2368 (2014)
Zhou, X., Yu, H., Liu, H., Li, Y.F.: Tracking multiple video targets with an improved GM-PHD tracker. Sensors 15(12), 30240–30260 (2015)
Zhou, X., Li, Y.F., He, B., Bai, T.: GM-PHD-Based multi-target visual tracking using entropy distribution and game theory. IEEE Trans. Ind. Inf. 10(2), 1064–1076 (2014)
Ma, C., Huang, J.-B., Yang, X., Yang, M.-H.: Hierarchical convolutional features for visual tracking. In: IEEE International Conference on Computer Vision, pp. 3074–3082. IEEE, Boston (2015)
Wang, N., Yeung, D.-Y.: Learning a deep compact image representation for visual tracking. In: Advances in Neural Information Processing Systems, pp. 809–817. Lake Tahoe (2013)
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4293–4302. IEEE, Las Vegas (2016)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Yang, F., Lu, H., Yang, M.H.: Robust superpixel tracking. IEEE Trans. Image Process. 23(4), 1639–1651 (2014)
Cheng, M.-M., Zhang, Z.B., et al.: Binarized normed gradients for objectness estimation at 300fps. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3286–3293. IEEE, Columbus (2014)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418. IEEE, Portland (2013)
Qi, Y., Zhang, S., Qin, L., et al.: Hedged deep tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4303–4311. IEEE, Las Vegas (2016)
Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33712-3_62
Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 983–990. IEEE, Miami (2009)
Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: bootstrapping binary classifiers by structural constraints. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 49–56. IEEE, California (2010)
Danelljan, M., Hager, G., Shahbaz Khan, F., et al.: Learning spatially regularized correlation filters for visual tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 4310–4318. IEEE, Boston (2015)
Acknowledgment
This work is partially supported by National Natural Science Foundation (Grant No. 61403342, U1509207, 61325019).
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Li, J., Zhou, X., Chen, S., Chan, S., Ju, Z. (2017). Robust Object Tracking via Structure Learning and Patch Refinement in Handling Occlusion. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10464. Springer, Cham. https://doi.org/10.1007/978-3-319-65298-6_41
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DOI: https://doi.org/10.1007/978-3-319-65298-6_41
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