publication . Preprint . Article . 2016

Geometric Hypergraph Learning for Visual Tracking

Dawei Du; Honggang Qi; Longyin Wen; Qi Tian; Qingming Huang; Siwei Lyu;
Open Access English
  • Published: 18 Mar 2016
Abstract
Graph-based representation is widely used in visual tracking field by finding correct correspondences between target parts in different frames. However, most graph-based trackers consider pairwise geometric relations between local parts. They do not make full use of the target’s intrinsic structure, thereby making the representation easily disturbed by errors in pairwise affinities when large deformation or occlusion occurs. In this paper, we propose a geometric hypergraph learning-based tracking method, which fully exploits high-order geometric relations among multiple correspondences of parts in different frames. Then visual tracking is formulated as the mode-...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction, Electrical and Electronic Engineering, Software, Information Systems, Computer Science Applications, Vertex (geometry), Pattern recognition, Hypergraph, Visualization, Pairwise comparison, Eye tracking, Scalability, Artificial intelligence, business.industry, business, Robustness (computer science), BitTorrent tracker, Mathematics, Machine learning, computer.software_genre, computer
Related Organizations
48 references, page 1 of 4

[1] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk. Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11):2274-2282, 2012.

[2] A. Adam, E. Rivlin, and I. Shimshoni. Robust fragments-based tracking using the integral histogram. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 798-805, 2006.

[3] B. Babenko, M.-H. Yang, and S. Belongie. Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8):1619-1632, 2011.

[4] W. Bouachir and G. Bilodeau. Collaborative part-based tracking using salient local predictors. Computer Vision and Image Understanding, 137:88-101, 2015. [OpenAIRE]

[5] Y. Boykov and V. Kolmogorov. An experimental comparison of mincut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9):1124- 1137, 2004. [OpenAIRE]

[6] Z. Cai, L. Wen, Z. Lei, N. Vasconcelos, and S. Z. Li. Robust deformable and occluded object tracking with dynamic graph. IEEE Transactions on Image Processing, 23(12):5497-5509, 2014.

[7] L. Cehovin, M. Kristan, and A. Leonardis. Robust visual tracking using an adaptive coupled-layer visual model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4):941-953, 2013.

[8] L. Cehovin, M. Kristan, and A. Leonardis. Is my new tracker really better than yours? In IEEE Winter Conference on Applications of Computer Vision, pages 540-547, 2014. [OpenAIRE]

[9] M. Danelljan, G. Ha¨ger, F. S. Khan, and M. Felsberg. Accurate scale estimation for robust visual tracking. In Proceedings of British Machine Vision Conference, 2014.

[10] M. Danelljan, F. Shahbaz Khan, M. Felsberg, and J. Van de Weijer. Adaptive color attributes for real-time visual tracking. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2014.

[11] S. Duffner and C. Garcia. Pixeltrack: A fast adaptive algorithm for tracking non-rigid objects. In Proceedings of the IEEE International Conference on Computer Vision, pages 2480-2487, 2013. [OpenAIRE]

[12] M. K. et al. The visual object tracking VOT2014 challenge results. In Workshops in Conjunction with European Conference on Computer Vision, pages 191-217, 2014.

[13] M. Godec, P. M. Roth, and H. Bischof. Hough-based tracking of nonrigid objects. In Proceedings of the IEEE International Conference on Computer Vision, pages 81-88, 2011.

[14] Y. Guo, Y. Chen, F. Tang, A. Li, W. Luo, and M. Liu. Object tracking using learned feature manifolds. Computer Vision and Image Understanding, 118:128-139, 2014.

[15] S. Hare, A. Saffari, and P. H. Torr. Struck: Structured output tracking with kernels. In Proceedings of the IEEE International Conference on Computer Vision, pages 263-270, 2011.

48 references, page 1 of 4
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