Struck: Structured Output Tracking with Kernels

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 38, Issue 10, 2016, Pages 2096-2109

  Hare, Sam ^a   Golodetz, Stuart ^b   Saffari, Amir ^c   Vineet, Vibhav ^d   Cheng, Ming Ming ^e   Hicks, Stephen L ^b   Torr, Philip H S ^b  

^a Obvious Engineering Limited   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c Importio   (United Kingdom)

^d Stanford University   (United States)

^e NANKAI UNIVERSITY   (China)

Author keywords

budget maintenance; GPU based tracking; structured output SVMs; Tracking by detection

Indexed keywords

BUDGET CONTROL; COMPUTER VISION; IMAGE PROCESSING; OBJECT RECOGNITION; TRACKING (POSITION);

BUDGETING MECHANISM; CLASSIFICATION TASKS; GPU-BASED; STRUCTURED OUTPUT PREDICTION; STRUCTURED OUTPUT SVMS; TRACKING BY DETECTIONS; TRACKING PERFORMANCE; VISUAL OBJECT TRACKING;

SUPPORT VECTOR MACHINES;

EID: 84986269170     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2015.2509974     Document Type: Article

References (51)

1. S. Avidan, "Support vector tracking, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 8, pp. 1064-1072, Aug. 2004.
2. H. Grabner, M. Grabner, and H. Bischof, "Real-time tracking via on-line Boosting, " in Proc. Brit. Mach. Vis. Conf., 2006, pp. 47-56.
3. B. Babenko, M.-H. Yang, and S. Belongie, "Robust object tracking with online multiple instance learning, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1619-1632, Aug. 2011.
4. A. Saffari, M. Godec, T. Pock, C. Leistner, and H. Bischof, "Online multi-class LPBoost, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 3570-3577.
5. O. Williams, A. Blake, and R. Cipolla, "A sparse probabilistic learning algorithm for real-time tracking, " in Proc. IEEE Int. Conf. Comput. Vis., 2003, pp. 353-360.
6. C. Leistner, A. Saffari, P. M. Roth, and H. Bischof, "On robustness of on-line boosting-a competitive study, " in Proc. IEEE 12th Int. Conf. Comput. Vis. Workshops, 2009, pp. 1362-1369.
7. H. Masnadi-Shirazi, V. Mahadevan, and N. Vasconcelos, "On the design of robust classifiers for computer vision, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 779-786.
8. H. Grabner, C. Leistner, and H. Bischof, "Semi-supervised on-line boosting for robust tracking, " in Proc. 10th Eur. Conf. Comput. Vis., 2008, pp. 234-247.
9. A. Saffari, C. Leistner, M. Godec, and H. Bischof, "Robust multiview boosting with priors, " in Proc. Eur. Conf. Comput. Vis., 2010, pp. 776-789.
10. B. Zeisl, C. Leistner, A. Saffari, and H. Bischof, "Online semisupervised multiple-instance boosting, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, p. 1879.
11. P. Viola and M. J. Jones, "Robust real-time face detection, " Int. J. Comput. Vis., vol. 57, no. 2, pp. 137-154, 2004.
12. M. B. Blaschko and C. H. Lampert, "Learning to localize objects with structured output regression, " in Proc. Eur. Conf. Comput. Vis., 2008, pp. 2-15.
13. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, "Multiple kernels for object detection, " in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 606-613.
14. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1627-1645, Sep. 2010.
15. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large margin methods for structured and interdependent output variables, " The J. Mach. Learn. Res., vol. 6, pp. 1453-1484, 2005.
16. A. Bordes, L. Bottou, P. Gallinari, and J. Weston, "Solving multiclass support vector machines with LaRank, " in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 89-96.
17. A. Bordes, N. Usunier, and L. Bottou, "Sequence labelling SVMs trained in one pass, " in Proc. Eur. Conf. Mach. Learn. Knowl. Discovery Databases, 2008, pp. 146-161.
18. K. Crammer, J. Kandola, R. Holloway, and Y. Singer, "Online classification on a budget, " in Proc. Adv. Neural Inf. Process. Syst., 2003, pp. 225-232.
19. Z. Wang, K. Crammer, and S. Vucetic, "Multi-class pegasos on a budget, " in Proc. Int. Conf. Mach. Learn., 2010, pp. 1143-1150.
20. S. Hare, A. Saffari, and P. H. S. Torr, "Struck: Structured output tracking with kernels, " in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 263-270.
21. Y. Wu, J. Lim, and M.-H. Yang, "Online object tracking: A benchmark, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2013, pp. 2411-2418.
22. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "High-speed tracking with kernelized correlation filters, " Proc. IEEE Trans. Pattern Anal. Mach. Intell., 2015, pp. 583-596.
23. A. W. M. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, "Visual tracking: An experimental survey, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 7, pp. 1442-1468, Jul. 2014.
24. Y. Pang and H. Ling, "Finding the best from the second bests-inhibiting subjective bias in evaluation of visual tracking algorithms, " in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 2784-2791.
25. M. Kristan, et al., "The visual object tracking VOT2014 challenge results, " in Proc. Eur. Conf. Comput. Vis. Workshop, 2014, pp. 191-217.
26. X. Mei, H. Ling, Y. Wu, E. Blasch, and L. Bai, "Minimum error bounded efficient '1 tracker with occlusion detection, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2011, pp. 1257-1264.
27. N. Wang, J. Wang, and D.-Y. Yeung, "Online robust non-negative dictionary learning for visual tracking, " in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 657-664.
28. J. Xing, J. Gao, B. Li, W. Hu, and S. Yan, "Robust object tracking with online multi-lifespan dictionary learning, " in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 665-672.
29. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, " IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174-188, Feb. 2002.
30. S. Cao and W. Xue, "Robust visual tracking via adaptive forest, " in Proc. 4th Int. Conf. Intell. Control Inf. Process., Jun. 2013, pp. 30-34.
31. A. Saffari, C. Leistner, J. Santner, M. Godec, and H. Bischof, "Online random forests, " in Proc. IEEE 12th Int. Conf. Comput. Vis. Workshops, 2009, pp. 1393-1400.
32. J. Santner, C. Leistner, A. Saffari, T. Pock, and H. Bischof, "PROST: Parallel robust online simple tracking, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 723-730.
33. Q. Bai, Z. Wu, S. Sclaroff, M. Betke, and C. Monnier, "Randomized ensemble tracking, " in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 2040-2047.
34. N. Wang and D.-Y. Yeung, "Ensemble-based tracking: Aggregating crowdsourced structured time series data, " in Proc. ICML, 2014, pp. 1107-1115.
35. S. Duffner and C. Garcia, "PixelTrack: A fast adaptive algorithm for tracking non-rigid objects, " in Proc. IEEE Int. Conf. Comput. Vis., 2013, pp. 2480-2487.
36. X. Ren and J. Malik, "Tracking as repeated figure/ground segmentation, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2007, pp. 1-8.
37. S. Wang, H. Lu, F. Yang, and M.-H. Yang, "Superpixel tracking, " in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 1323-1330.
38. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "Exploiting the circulant structure of tracking-by-detection with kernels, " in Proc. Eur. Conf. Comput. Vis., 2012, pp. 702-715.
39. M. Danelljan, F. S. Khan, M. Felsberg, and J. van de Weijer, "Adaptive color attributes for real-time visual tracking, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2014, pp. 1090-1097.
40. R. Yao, Q. Shi, C. Shen, Y. Zhang, and A. van den Hengel, "Partbased visual tracking with online latent structural learning, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2013, pp. 2363-2370.
41. C. Shen, G. Lin, and A. van den Hengel, "StructBoost: Boosting methods for predicting structured output variables, " arXiv:1302. 3283v3, Feb. 2014.
42. S. Kim, S. Kwak, J. Feyereisl, and B. Han, "Online multi-target tracking by large margin structured learning, " in Proc. 11th Asian Conf. Comput. Vis., 2012, pp. 98-111.
43. F. Pernici and A. D. Bimbo, "Object tracking by oversampling local features, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 12, pp. 2538-2551, Dec. 2014.
44. Y. Lu, T. Wu, and S.-C. Zhu, "Online object tracking, learning and parsing with and-or graphs, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2014, pp. 3462-3469.
45. L. Zhang and L. van der Maaten, "Preserving structure in modelfree tracking, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 4, pp. 756-769, Apr. 2014.
46. C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2007, pp. 707-710.
47. J. C. Platt, Fast Training of Support Vector Machines Using Sequential Minimal Optimization. Cambridge, MA, USA: MIT Press, 1999, pp. 185-208.
48. P. Gehler and S. Nowozin, "On feature combination for multiclass object classification, " in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 221-228.
49. W. Zhong, H. Lu, and M.-H. Yang, "Robust object tracking via sparsity-based collaborative model, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2012, pp. 1838-1845.
50. Z. Kalal, J. Matas, and K. Mikolajczyk, "P-N learning: Bootstrapping binary classifiers by structural constraints, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2010, pp. 49-56.
51. X. Jia, H. Lu, and M.-H. Yang, "Visual tracking via adaptive structural local sparse appearance model, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2012, pp. 1822-1829.