Learning spatially regularized correlation filters for visual tracking

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 4310-4318

  Danelljan, Martin ^a   Hager, Gustav ^a   Khan, Fahad Shahbaz ^a   Felsberg, Michael ^a  

^a LINKÖPING UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

BANDPASS FILTERS; COMPUTER VISION; ITERATIVE METHODS; SAMPLING; TRACKING (POSITION);

APPEARANCE MODELING; BENCHMARK DATASETS; COMPUTER VISION PROBLEMS; CORRELATION FILTERS; GAUSS-SEIDEL METHOD; OPTIMIZATION STRATEGY; ROBUST APPROACHES; SPATIAL REGULARIZATIONS;

FACE RECOGNITION;

EID: 84973922861     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.490     Document Type: Conference Paper

References (37)

1. A. Adam, E. Rivlin, and Shimshoni. Robust fragments-based tracking using the integral histogram. In CVPR, 2006.
2. B. Babenko, M.-H. Yang, and S. Belongie. Visual tracking with online multiple instance learning. In CVPR, 2009.
3. C. Bao, Y. Wu, H. Ling, and H. Ji. Real time robust l1 tracker using accelerated proximal gradient approach. In CVPR, 2012.
4. V. N. Boddeti, T. Kanade, and B. V. K. V. Kumar. Correlation filters for object alignment. In CVPR, 2013.
5. D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui. Visual object tracking using adaptive correlation filters. In CVPR, 2010.
6. C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273-297, 1995.
7. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
8. M. Danelljan, G. Häger, F. S. Khan, and M. Felsberg. Accurate scale estimation for robust visual tracking. In BMVC, 2014.
9. M. Danelljan, G. Häger, F. S. Khan, and M. Felsberg. Coloring channel representations for visual tracking. In SCIA, 2015.
10. M. Danelljan, F. S. Khan, M. Felsberg, and J. van de Weijer. Adaptive color attributes for real-time visual tracking. In CVPR, 2014.
11. T. B. Dinh, N. Vo, and G. Medioni. Context tracker: Exploring supporters and distracters in unconstrained environments. In CVPR, 2011.
12. M. Felsberg. Enhanced distribution field tracking using channel representations. In ICCV Workshop, 2013.
13. H. K. Galoogahi, T. Sim, and S. Lucey. Multi-channel correlation filters. In ICCV, 2013.
14. H. K. Galoogahi, T. Sim, and S. Lucey. Correlation filters with limited boundaries. In CVPR, 2015.
15. J. Gao, H. Ling, W. Hu, and J. Xing. Transfer learning based visual tracking with Gaussian process regression. In ECCV, 2014.
16. S. Hare, A. Saffari, and P. Torr. Struck: Structured output tracking with kernels. In ICCV, 2011.
17. S. He, Q. Yang, R. Lau, J. Wang, and M.-H. Yang. Visual tracking via locality sensitive histograms. In CVPR, 2013.
18. J. F. Henriques, J. Carreira, R. Caseiro, and J. Batista. Beyond hard negative mining: Efficient detector learning via block-circulant decomposition. In ICCV, 2013.
19. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. Exploiting the circulant structure of tracking-by-detection with kernels. In ECCV, 2012.
20. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. Highspeed tracking with kernelized correlation filters. PAMI, 2015.
21. X. Jia, H. Lu, and M.-H. Yang. Visual tracking via adaptive structural local sparse appearance model. In CVPR, 2012.
22. Z. Kalal, J. Matas, and K. Mikolajczyk. P-n learning: Bootstrapping binary classifiers by structural constraints. In CVPR, 2010.
23. M. Kristan, R. Pflugfelder, A. Leonardis, J. Matas, and et al. The visual object tracking vot2014 challenge results. In ECCV Workshop, 2014.
24. Y. Li and J. Zhu. A scale adaptive kernel correlation filter tracker with feature integration. In ECCV Workshop, 2014.
25. OpenCV. The opencv state of the art vision challenge. http://code. opencv. org/projects/opencv/ wiki/VisionChallenge. Accessed: 2015-09-17.
26. S. Oron, A. Bar-Hillel, D. Levi, and S. Avidan. Locally orderless tracking. In CVPR, 2012.
27. P. Perez, C. Hue, J. Vermaak, and M. Gangnet. Color-based probabilistic tracking. In ECCV, 2002.
28. D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang. Incremental learning for robust visual tracking. IJCV, 77(1):125-141, 2008.
29. L. Sevilla-Lara and E. G. Learned-Miller. Distribution fields for tracking. In CVPR, 2012.
30. A. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah. Visual tracking: An experimental survey. PAMI, 36(7):1442-1468, 2014.
31. J. van de Weijer, C. Schmid, J. J. Verbeek, and D. Larlus. Learning color names for real-world applications. TIP, 18(7):1512-1524, 2009.
32. D. Wang, H. Lu, and M.-H. Yang. Least soft-threshold squares tracking. In CVPR, 2013.
33. Y. Wu, J. Lim, and M.-H. Yang. Online object tracking: A benchmark. In CVPR, 2013.
34. Y. Wu, J. Lim, and M.-H. Yang. Object tracking benchmark. PAMI, 2015.
35. J. Zhang, S. Ma, and S. Sclaroff. MEEM: robust tracking via multiple experts using entropy minimization. In ECCV, 2014.
36. K. Zhang, L. Zhang, and M. Yang. Real-time compressive tracking. In ECCV, 2012.
37. W. Zhong, H. Lu, and M.-H. Yang. Robust object tracking via sparsity-based collaborative model. In CVPR, 2012.