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Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
This paper proposes a novel approach to part-based track- ing by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations. We propose to use cascaded regression with incremental learning to track generic objects without any prior knowledge of an object’s structure or appearance. We exploit the spatial constraints between parts by implicitly learning the shape and deformation parameters of the object in an online fashion. We integrate a multiple temporal scale motion model to initialise our cascaded regression search close to the target and to allow it to cope with occlusions. Experimental results show that our tracker ranks first on the CVPR 2013 Benchmark.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] A. Asthana, S. Zafeiriou, S. Cheng, and M. Pantic. Incremental face alignment in the wild. In Computer Vision and Pattern Recognition, 2014. 2, 5
    • [2] B. Babenko, M.-H. Yang, and S. Belongie. Robust object tracking with online multiple instance learning. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(8):1619-1632, Aug 2011. 6
    • [3] T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. In Computer Vision - ECCV'98, 5th European Conference on Computer Vision, Freiburg, Germany, June 2-6, 1998, Proceedings, Volume II , pages 484-498, 1998. 2
    • [4] D. Crandall, P. Felzenszwalb, and D. Huttenlocher. Spatial priors for part-based recognition using statistical models. In Computer Vision and Pattern Recognition, 2005. 3
    • [5] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, pages 886-893, 2005. 6
    • [6] L. Ellis, N. Dowson, J. Matas, and R. Bowden. Linear regression and adaptive appearance models for fast simultaneous modelling and tracking. Int'l Journal of Computer Vision, 95(2):154-179, 2011. 3
    • [7] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. Trans. on Pattern Analysis and Machine Intelligence, 32(9):1627-1645, 2010. 3
    • [8] S. Hare, A. Saffari, and P. Torr. Struck: Structured output tracking with kernels. In Int'l Conf. Computer Vision, pages 263-270, 2011. 3, 8
    • [9] J. a. F. Henriques, R. Caseiro, P. Martins, and J. Batista. Exploiting the circulant structure of tracking-by-detection with kernels. In Proceedings of the 12th European Conference on Computer Vision - Volume Part IV , ECCV'12, pages 702- 715, Berlin, Heidelberg, 2012. Springer-Verlag. 6
    • [10] A. E. Hoerl and R. W. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12:55- 67, 1970. 5
    • [11] M. H. Khan, M. F. Valstar, and T. P. Pridmore. Mts: A multiple temporal scale tracker handling occlusion and abrupt motion variation. In Asian Conf. on Computer Vision, 2015. 2, 5
    • [12] J. Kwon and K. M. Lee. Highly nonrigid object tracking via patch-based dynamic appearance modeling. Trans. on Pattern Analysis and Machine Intelligence, 35(10):2427-2441, 2013. 3
    • [13] B. Leibe, A. Leonardis, and B. Schiele. Robust object detection with interleaved categorization and segmentation. Int'l Journal of Computer Vision, 77:259-289, 2008. 3
    • [14] B. Martinez, M. Valstar, X. Binefa, and M. Pantic. Local evidence aggregation for regression based facial point detection. Transactions on Pattern Analysis and Machine Intelligence, 35(5):1149-1163, 2013. 2, 3, 5
    • [15] E.-J. Ong and R. Bowden. Robust facial feature tracking using shape-constrained multiresolution-selected linear predictors. IEEE Trans. Pattern Anal. Mach. Intell., 33(9):1844- 1859, 2011. 3
    • [16] I. Patras and E. R. Hancock. Coupled prediction classification for robust visual tracking. Trans. on Pattern Analysis and Machine Intelligence, 32(9):1553-1567, 2010. 3
    • [17] D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang. Incremental learning for robust visual tracking. Int'l Journal of Computer Vision, 77(1-3):125-141, 2008. 5
    • [18] S. Shahed Nejhum, J. Ho, and M.-H. Yang. Visual tracking with histograms and articulating blocks. In Computer Vision and Pattern Recognition, 2008. 3
    • [19] I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6:1453-1484, 2005. 3
    • [20] O. Williams, A. Blake, and R. Cipolla. Sparse bayesian learning for efficient visual tracking. Trans. on Pattern Analysis and Machine Intelligence, 27(8):1292-1304, 2005. 3
    • [21] Y. Wu, J. Lim, and M.-H. Yang. Online object tracking: A benchmark. In Computer Vision and Pattern Recognition, 2013. 2, 6, 7, 8
    • [22] Xuehan-Xiong and F. De la Torre. Supervised descent method and its application to face alignment. In Computer Vision and Pattern Recognition, 2013. 2, 3, 4
    • [23] M. Yang, J. Yuan, and Y. Wu. Spatial selection for attentional visual tracking. In Computer Vision and Pattern Recognition, 2007. 3
    • [24] R. Yao, Q. Shi, C. Shen, Y. Zhang, and A. van den Hengel. Part-based visual tracking with online latent structural learning. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 2363-2370. IEEE, 2013. 3
    • [25] L. Zhang and L. van der Maaten. Structure preserving object tracking. In Computer Vision and Pattern Recognition, pages 1838-1845, 2013. 3
    • [26] W. Zhong. Robust object tracking via sparsity-based collaborative model. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), CVPR '12, pages 1838-1845, Washington, DC, USA, 2012. IEEE Computer Society. 8
    • [27] K. Zimmermann, J. Matas, and T. Svoboda. Tracking by an optimal sequence of linear predictors. Trans. on Pattern Analysis and Machine Intelligence, 31(4):677-692, 2009. 3
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