© The Institution of Engineering and Technology
The authors extend exemplar representation to the field of tracking and propose a robust tracking algorithm with per-exemplar support vector machine (SVM) classifiers. First, the authors train the simple yet effective exemplar SVM classifier using the target object as the single positive and mining its surroundings as hard negatives. Second, the authors propose an online ensemble tracker, which integrates the useful ‘key historical templates’ of the target to refine the current template, leading to better discriminative power of tracker and effectively decreasing the risk of drift. Experiments on challenging sequences demonstrate that the tracker performs well in accuracy and robustness, especially under the sequences with strong illumination variation and scale variation, as well as pose change and partial occlusion in the long-time sequence.
References
-
-
1)
-
18. Zhang, K.H., Zhang, L., Yang, M.H.: ‘Real-time compressive tracking’. European Conf. on Computer Vision, 2012, pp. 864–877.
-
2)
-
7. Zhang, S., Yao, H., Sun, X., Liu, S.: ‘Robust visual tracking using an effective appearance model based on sparse coding’, ACM Trans. Intell. Syst. Technol., 2012, 3, (3), pp. 43–50.
-
3)
-
19. Grabner, H., Leistner, C., Bischof, H.: ‘Semi-supervised on-line boosting for robust tracking’. European Conf. on Computer Vision, 2008, pp. 234–247.
-
4)
-
16. Bai, Q., Wu, Z., Sclaroff, S., Betke, M., Monnier, C.: ‘Randomized ensemble tracking’. IEEE Int. Conf. on Computer Vision, 2013.
-
5)
-
17. Wang, Q., Chen, F., Xu, W., Yang, M.H.: ‘Online discriminative object tracking with local sparse representation’. IEEE Workshop on Applications of Computer Vision, 2012.
-
6)
-
29. Khan, F.S., Anwer, R.M., van de Weijer, J., Bagdanov, A.D., Vanrell, M., Lopez, A.M.: ‘Color attributes for object detection’. IEEE Conf. on Computer Vision and Pattern Recognition, 2012.
-
7)
-
3. Jacob, R.J.K., Karn, K.S.: ‘Eye tracking in human-computer interaction and usability research: Ready to deliver the promises’, Mind, 2003, 2, (3), pp. 525–528.
-
8)
-
2. Avidan, S.: ‘Support vector tracking’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (8), pp. 1064–1072 (doi: 10.1109/TPAMI.2004.53).
-
9)
-
5. Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: ‘Incremental learning for robust visual tracking’, Int. J. Comput. Vis., 2008, 77, (1), pp. 125–141 (doi: 10.1007/s11263-007-0075-7).
-
10)
-
31. van de Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: ‘Learning color names for real-world applications’. IEEE Transactions in Image Processing, 2009.
-
11)
-
6. Sun, X., Zhang, J., Xie, Z., Gao, J.: ‘Active-matting-based object tracking with color cues’. Signal, Image and Video Processing, 2014, pp. 1–10.
-
12)
-
35. Babenko, B., Yang, M.H., Belongie, S.: ‘Robust object tracking with online multiple instance learning’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (8), pp. 1619–1632 (doi: 10.1109/TPAMI.2010.226).
-
13)
-
10. Zhang, S., Zhou, H., Yao, H., Zhang, Y., Wang, K., Zhang, J.: ‘Adaptive normalhedge for robust visual tracking’. Signal Processing, 2015, .
-
14)
-
23. Brady, T.F., Konkle, T., Alvare, G.A., Oliva, A.: ‘Visual long-term memory has a massive storage capacity for object details’, Proc. Natl. Acad. Sci., 2008, 105, (38), pp. 14325–14329 (doi: 10.1073/pnas.0803390105).
-
15)
-
24. Malisiewicz, T., Shrivastava, A., Gupta, A., Efros, A.A.: ‘Exemplar-svms for visual object detection, label transfer and image retrieval’. Int. Conf. on Machine Learning, 2012.
-
16)
-
33. Bao, C., Wu, Y., Ling, H., Ji, H.: ‘Real time robust 11 tracker using accelerated proximal gradient approach’. Computer Vision and Pattern Recognition, 2012, pp. 1830–1837.
-
17)
-
4. Wu, Y., Lim, J., Yang, M.H.: ‘Online object tracking: A benchmark’. IEEE Conf. on Computer Vision and Pattern Recognition, 2013.
-
18)
-
8. Zhang, S., Yao, H., Sun, X., Lu, X.: ‘Sparse coding based visual tracking: Review and experimental comparison’, Pattern Recognit., 2013, 46, (7), pp. 1772–1788 (doi: 10.1016/j.patcog.2012.10.006).
-
19)
-
34. Yang, H., Shao, L., Zheng, F., Wang, L., Song, Z.: ‘Recent advances and trends in visual tracking: A review’, Neurocomputing, 2011, 74, (18), pp. 3823–3831 (doi: 10.1016/j.neucom.2011.07.024).
-
20)
-
11. Grabner, H., Bischof, H.: ‘On-line boosting and vision’. IEEE Conf. on Computer Vision and Pattern Recognition, 2006, vol. 1, pp. 260–267.
-
21)
-
15. Malisiewicz, T., Gupta, A., Efros, A.A.: ‘Ensemble of exemplar-svms for object detection and beyond’. IEEE Int. Conf. on Computer Vision, 2011.
-
22)
-
13. Hare, S., Saffari, A., Torr, P.H.S.: ‘Struck: Structured output tracking with kernels’. IEEE Int. Conf. on Computer Vision, 2011.
-
23)
-
32. Oron, S., Bar-Hillel, A., Levi, D., Avidan, S.: ‘Locally orderless tracking’. Computer Vision and Pattern Recognition, 2012, pp. 1940–1947.
-
24)
-
25. Frome, A., Singer, Y., Malik, J.: ‘Image retrieval and classication using local distance functions’. Proc. of the 2006 Conf. on Advances in Neural Information Processing Systems 19, 2007, vol. 19.
-
25)
-
14. Okuma, K., Taleghani, A., Freitas, N.D., Little, J.J., Lowe, D.G.: ‘A boosted particle filter: Multitarget detection and tracking’. European Conf. on Computer Vision, 2004, pp. 28–39.
-
26)
-
9. Zhang, S., Yao, H., Zhou, H., Sun, X., Liu, S.: ‘Robust visual tracking based on online learning sparse representation’, Neurocomputing, 2013, 100, pp. 31–40 (doi: 10.1016/j.neucom.2011.11.031).
-
27)
-
1. Stauffer, C., Grimson, W.E.L.: ‘Learning patterns of activity using real-time tracking’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (8), pp. 747–757 (doi: 10.1109/34.868677).
-
28)
-
26. Malisiewicz, T., Efros, A.A.: ‘Recognition by association via learning per-exemplar distances’. IEEE Conf. on Computer Vision and Pattern Recognition, 2008.
-
29)
-
20. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: ‘Exploiting the circulant structure of tracking-by-detection with kernels’. European Conf. on Computer Vision, 2012, pp. 702–715.
-
30)
-
12. Babenko, B., Yang, M.H., Belongie, S.: ‘Visual tracking with online multiple instance learning’. IEEE Conf. on Computer Vision and Pattern Recognition, 2009.
-
31)
-
21. Sevilla-Lara, L., Learned-Miller, E.: ‘Distribution fields for tracking’. IEEE Conf. on Computer Vision and Pattern Recognition, 2012.
-
32)
-
28. Joze, H.R.V., Drew, M.S.: ‘Exemplar-based colour constancy and multiple illumination’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36, (5), pp. 860–873 (doi: 10.1109/TPAMI.2013.169).
-
33)
-
30. Villamizar, M., Scandaliaris, J., Sanfeliu, A., Andrade-Cetto, J.: ‘Combining color-based invariant gradient detector with hog descriptors for robust image detection in scenes under cast shadows’. IEEE Int. Conf. on Robotics and Automation, 2009.
-
34)
-
22. Lawry, J., Tang, Y.: ‘Relating prototype theory and label semantics’. Soft Methods for Handling Variability and Imprecision, 2008, pp. 35–42.
-
35)
-
27. Tighe, J., Lazebnik, S.: ‘Finding things: Image parsing with regions and per-exemplar detectors’. IEEE Conf. on Computer Vision and Pattern Recognition, 2013.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2014.0234
Related content
content/journals/10.1049/iet-cvi.2014.0234
pub_keyword,iet_inspecKeyword,pub_concept
6
6