The visual object tracking VOT2013 challenge results

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 98-111

  Kristan, Matej ^a   Pflugfelder, Roman ^b   Leonardis, Aleš ^c   Matas, Jiri ^d   Porikli, Fatih ^e   Čehovin, Luka ^a   Nebehay, Georg ^b   Fernandez, Gustavo ^b   Vojíř, Tomáš ^d   Gatt, Adam ^f   Khajenezhad, Ahmad ^g   Salahledin, Ahmed ^h   Soltani Farani, Ali ^g   Zarezade, Ali ^g   Petrosino, Alfredo ⁱ   Milton, Anthony ^j   Bozorgtabar, Behzad ^k   Li, Bo ^l   Chan, Chee Seng ^m   Heng, Cherkeng ^l   Ward, Dale ^j   Kearney, David ^j   Monekosso, Dorothy ⁿ   Karaimer, Hakki Can ^o   Rabiee, Hamid R ^g   Zhu, Jianke ^p   Gao, Jin ^q   Xiao, Jingjing ^c   Zhang, Junge ^r   Xing, Junliang ^q   Huang, Kaiqi ^r   Lebeda, Karel ^s   Cao, Lijun ^r   Maresca, Mario Edoardo ⁱ   Lim, Mei Kuan ^m   ELHelw, Mohamed ^h   Felsberg, Michael ^t   Remagnino, Paolo ^u   Bowden, Richard ^s   Goecke, Roland ^e   Stolkin, Rustam ^c   Lim, Samantha Yue Ying ^a   Maher, Sara ^h   Poullot, Sebastien ^v   Wong, Sebastien ^f   Satoh, Shin'Ichi ^w   Chen, Weihua ^r   Hu, Weiming ^q   Zhang, Xiaoqin ^q   Li, Yang ^p   Niu, Zhiheng ^a   more..

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b AIT AUSTRIAN INSTITUTE OF TECHNOLOGY   (Austria)

^c UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^d CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

^e AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^f DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION   (Australia)

^g SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

^h NILE UNIVERSITY   (Egypt)

ⁱ UNIVERSITY OF NAPLES PARTHENOPE   (Italy)

^j UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

^k UNIVERSITY OF CANBERRA   (Australia)

^l Panasonic RandD Center   (Singapore)

^m UNIVERSITY OF MALAYA   (Malaysia)

ⁿ UNIVERSITY OF THE WEST OF ENGLAND   (United Kingdom)

^o IZMIR INSTITUTE OF TECHNOLOGY   (Turkey)

^p ZHEJIANG UNIVERSITY   (China)

^q INSTITUTE OF AUTOMATION   (China)

^r INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^s UNIVERSITY OF SURREY   (United Kingdom)

^t LINKÖPING UNIVERSITY   (Sweden)

^u KINGSTON UNIVERSITY   (United Kingdom)

^v National Institute of Informatics   (Japan)

^w NII JFLI ^*  (Japan)

more..

Author keywords

Visual object tracking challenge; VOT2013

Indexed keywords

COMPUTER SCIENCE; COMPUTERS; ELECTRICAL ENGINEERING;

AUTOMATED SYSTEMS; BENCHMARK DATASETS; EVALUATION PROTOCOL; ILLUMINATION CHANGES; OBJECT APPEARANCE; VISUAL ATTRIBUTES; VISUAL OBJECT TRACKING; VOT2013;

AUTOMATION;

EID: 84897510119     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2013.20     Document Type: Conference Paper

References (55)

1. B. N. A. Lee and L. Wasserman. Treelets - an adaptive multi-scale basis for sparse unordered data. Ann. Appl. Stat., 2(2):435-471, 2008.
2. B. Babenko, M.-H. Yang, and S. Belongie. Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell., 33(8):1619-1632, 2011.
3. S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black, and S. R. A database and evaluation methodology for optical flow. Int. J. Comput. Vision, 92(1):1-31, 2011.
4. Y. Bar-Shalom, X. R. Li, and T. Kirubarajan. Estimation with Applications to Tracking and Navigation, chapter 11, pages 438-440. John Wiley & Sons, Inc., 2001.
5. W. Chen, L. Cao, J. Zhang, and K. Huang. An adaptive combination of multiple features for robust tracking in real scene. In Vis. Obj. Track. Challenge VOT2013, In conjunction with ICCV2013, 2013.
6. R. Collins, X. Zhou, and S. K. Teh. An open source tracking testbed and evaluation web site. In Perf. Eval. Track. and Surveillance, 2005.
7. D. Comaniciu, V. Ramesh, and P. Meer. Kernel-based object tracking. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(5):564-577, 2003.
8. H. L. D.Wang and M.-H. Yang. Least soft-threshold squares tracking. In In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013), Portland, June 2013.
9. J. Demšar. Statistical comparisons of classifiers over multiple datasets. 7:1-30, 2006.
10. J. Demšar. On the appropriateness of statistical tests in machine learning. In Workshop on Evaluation Methods for Machine Learning in conjunction with ICML, 2008.
11. D. Doermann and D. Mihalcik. Tools and techniques for video performance evaluation. In Proc. Int. Conf. Pattern Recognition, page 167170, 2000.
12. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. Int. J. Comput. Vision, 88(2):303-338, June 2010.
13. M. Felsberg. Enhanced distribution field tracking using channel representations. In Vis. Obj. Track. Challenge VOT2013, In conjunction with ICCV2013, 2013.
14. P. Gabriel, J. Verly, J. Piater, and A. Genon. The state of the art in multiple object tracking under occlusion in video sequences. In Proc. Advanced Concepts for Intelligent Vision Systems, page 166173, 2003.
15. J. Gao, J. Xing, W. Hu, and X. Zhang. Graph embedding based semi-supervised discriminative tracker. In Vis. Obj. Track. Challenge VOT2013, In conjunction with ICCV2013, 2013.
16. A. Gatt, S. Wong, and D. Kearney. Combining online feature selection with adaptive shape estimation. In Image and Vision Computing New Zealand (IVCNZ), 2010 25th International Conference of, pages 1-8. IEEE, 2010.
17. D. M. Gavrila. The visual analysis of human movement: A survey. Comp. Vis. Image Understanding, 73(1):82-98, 1999.
18. M. Godec, P. M. Roth, and H. Bischof. Hough-based tracking of non-rigid objects. Comp. Vis. Image Understanding, 117(10):1245-1256, 2013.
19. N. Goyette, P.-M. Jodoin, F. Porikli, J. Konrad, and P. Ishwar. Changedetection.net: A new change detection benchmark dataset. In CVPR Workshops, pages 1-8. IEEE, 2012.
20. S. Hare, A. Saffari, and P. H. S. Torr. Struck: Structured output tracking with kernels. In D. N. Metaxas, L. Quan, A. Sanfeliu, and L. J. V. Gool, editors, Int. Conf. Computer Vision, pages 263-270. IEEE, 2011.
21. C.-K. Heng, S. Yokomitsu, Y. Matsumoto, and H. Tamura. Shrink boost for selecting multi-lbp histogram features in object detection. In Comp. Vis. Patt. Recognition, pages 3250-3257, 2012.
22. W. Hu, T. Tan, L.Wang, and S. Maybank. A survey on visual surveillance of object motion and behaviors. IEEE Trans. Systems, Man and Cybernetics, C, 34(30):334-352, 2004.
23. C. Jaynes, S. Webb, R. Steele, and Q. Xiong. An open development environment for evaluation of video surveillance systems. In PETS, 2002.
24. Z. Kalal, J. Matas, and K. Mikolajczyk. P-n learning: Bootstrapping binary classifiers by structural constraints. In Comp. Vis. Patt. Recognition, pages 49-56. IEEE, 2010.
25. Z. Kalal, K. Mikolajczyk, and J. Matas. Tracking-learningdetection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(7):1409-1422, 2012.
26. R. Kasturi, D. B. Goldgof, P. Soundararajan, V. Manohar, J. S. Garofolo, R. Bowers, M. Boonstra, V. N. Korzhova, and J. Zhang. Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol. IEEE Trans. Pattern Anal. Mach. Intell., 31(2):319-336, 2009.
27. M. Kristan, S. Kovacic, A. Leonardis, and J. Pers. A twostage dynamic model for visual tracking. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 40(6):1505-1520, 2010.
28. M. Kristan, J. Perš, M. Perše, M. Bon, and S. Kovačič. Multiple interacting targets tracking with application to team sports. In International Symposium on Image and Signal Processing and Analysis, pages 322-327, September 2005.
29. J. Kwon and K. M. Lee. Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping monte carlo sampling. In Comp. Vis. Patt. Recognition, pages 1208-1215. IEEE, 2009.
30. K. Lebeda, R. Bowden, and J. Matas. Long-term tracking through failure cases. In Vis. Obj. Track. Challenge VOT2013, In conjunction with ICCV2013, 2013.
31. H. Li, C. Shen, and Q. Shi. Real-time visual tracking using compressive sensing. In Comp. Vis. Patt. Recognition, pages 1305-1312. IEEE, 2011.
32. X. Li,W. Hu, C. Shen, Z. Zhang, A. R. Dick, and A. van den Hengel. A survey of appearance models in visual object tracking. arXiv:1303.4803 [cs.CV], 2013.
33. M. Lim, C. Chan, D. Monekosso, and P. Remagnino. Swatrack: A swarm intelligence-based abrupt motion tracker. In In proceedings of IAPR MVA, page pages 3740, 2013.
34. M. E. Maresca and A. Petrosino. Matrioska: A multi-level approach to fast tracking by learning. In Proc. Int. Conf. Image Analysis and Processing, pages 419-428, 2013.
35. T. B. Moeslund and E. Granum. A survey of computer vision-based human motion capture. Comp. Vis. Image Understanding, 81(3):231-268, March 2001.
36. T. B. Moeslund, A. Hilton, and V. Kruger. A survey of advances in vision-based human motion capture and analysis. Comp. Vis. Image Understanding, 103(2-3):90-126, November 2006.
37. T. Nawaz and A. Cavallaro. A protocol for evaluating video trackers under real-world conditions. IEEE Trans. Image Proc., 22(4):1354-1361, 2013.
38. Y. Pang and H. Ling. Finding the best from the second bests - inhibiting subjective bias in evaluation of visual tracking algorithms. In Comp. Vis. Patt. Recognition, 2013.
39. P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss. The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 22(10):1090-1104, 2000.
40. D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang. Incremental learning for robust visual tracking. Int. J. Comput. Vision, 77(1-3):125-141, 2008.
41. A. Salaheldin, S. Maher, and M. E. Helw. Robust real-time tracking with diverse ensembles and random projections. In Vis. Obj. Track. Challenge VOT2013, In conjunction with ICCV2013, 2013.
42. L. Sevilla-Lara and E. G. Learned-Miller. Distribution fields for tracking. In Comp. Vis. Patt. Recognition, pages 1910-1917. IEEE, 2012.
43. A. Torralba and A. A. Efros. Unbiased look at dataset bias. In Comp. Vis. Patt. Recognition, pages 1521-1528. IEEE, 2011.
44. L. Cehovin,M. Kristan, and A. Leonardis. Is my new tracker really better than yours? Technical Report 10, ViCoS Lab, University of Ljubljana, Oct 2013.
45. L. Cehovin, M. Kristan, and A. Leonardis. Robust visual tracking using an adaptive coupled-layer visual model. IEEE Trans. Pattern Anal. Mach. Intell., 35(4):941-953, 2013.
46. T. Vojir and J. Matas. Robustifying the flock of trackers. In Comp. Vis. Winter Workshop, pages 91-97. IEEE, 2011.
47. C. Wu, J. Zhu, J. Zhang, C. Chen, and D. Cai. A convolutional treelets binary feature approach to fast keypoint recognition. In ECCV, pages 368-382, 2013.
48. H. Wu, A. C. Sankaranarayanan, and R. Chellappa. Online empirical evaluation of tracking algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 32(8):1443-1458, 2010.
49. Y. Wu, J. Lim, and M. H. Yang. Online object tracking: A benchmark. In Comp. Vis. Patt. Recognition, 2013.
50. Y. Wu, B. Shen, and H. Ling. Online robust image alignment via iterative convex optimization. In Comp. Vis. Patt. Recognition, pages 1808-1814. IEEE, 2012.
51. J. Xiao, R. Stolkin, and A. Leonardis. An enhanced adaptive coupled-layer LGTracker++. In Vis. Obj. Track. Challenge VOT2013, In conjunction with ICCV2013, 2013.
52. A. Yilmaz and M. Shah. Object tracking: A survey. Journal ACM Computing Surveys 38(4), 2006.
53. D. P. Young and J. M. Ferryman. Pets metrics: On-line performance evaluation service. In ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks, pages 317-324, 2005.
54. A. Zarezade, H. R. Rabiee, A. Soltani-Frani, and A. Khajenezhad. Patchwise joint sparse tracker with occlusion detection using adaptive markov model. preprint in arXiv, 2013.
55. K. Zhang, L. Zhang, and M.-H. Yang. Real-time compressive tracking. In Proc. European Conf. Computer Vision, Lecture Notes in Computer Science, pages 864-877. Springer, 2012.