A new framework for on-line object tracking based on SURF

Pattern Recognition Letters

Volumn 32, Issue 13, 2011, Pages 1564-1571

  Miao, Quan ^a   Wang, Guijin ^a   Shi, Chenbo ^a   Lin, Xinggang ^a   Ruan, Zhiwei ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Adaptive classifiers; Keypoint matching; Object tracking; On line boosting; SURF

Indexed keywords

ADAPTIVE CLASSIFIERS; KEYPOINT MATCHING; OBJECT TRACKING; ON-LINE BOOSTING; SURF;

FEATURE EXTRACTION;

EID: 79960212498     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.05.017     Document Type: Article

References (33)

1. S. Avidan Support vector tracking IEEE Trans. Pattern Anal. Machine Intell. 26 2004 1064 1072
2. Avidan, S., 2005. Ensemble tracking. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 494-501.
3. Bay, H., Tuytelaars, T., Van Gool, L., 2006. SURF: Speed up robust features. In: Proc. European Conf. of Computer Vision (ECCV), pp. 404-417.
4. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool Speeded-up robust features (SURF) Comput. Vision Image Understanding 110 3 2008 346 359
5. M. Brown, and D. Lowe Automatic panoramic image stitching using invariant features Int. J. Comput. Vision 74 2007 59 77
6. Chun, O., Matas, J., 2005. Matching with PROSAC - progressive sample consensus. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 220-226.
7. R. Collins, and Y. Liu Online selection of discriminative tracking features IEEE Trans. Pattern Anal. Machine Intell. 27 10 2005 1631 1643 (Pubitemid 41555407)
8. Y. Freund, and R. Schapire A decision-theoretic generalization of on-line learning and an application to boosting J. Comput. System Sci. 55 1 1997 119 139 (Pubitemid 127433398)
9. Grabner, H., Bischof, H., 2006. On-line boosting and vision. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 260-267. (Pubitemid 44931339)
10. Grabner, M., Grabner, H., Bischof, H., 2007. Learning features for tracking. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).
11. Grabner, H., Leistner, C., Bischof, H., 2008. Semi-supervised on-line boosting for robust tracking. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).
12. Harris, C., Stephens, M., 1988. A combined corner and edge detector. Alevy Vision Conference, pp. 147-151.
13. R.I. Hartley, and A. Zisserman Multiple View Geometry in Computer Vision second ed. 2004 Cambridge University Press
14. Kalal, Z., Matas, J., Mikolajczyk, K., 2008. Weighted sampling for large-scale boosting. In: Proc. British Machine Vision Conf. (BMVC).
15. Laptev, I., 2006. Improvements of object detection using boosted histograms. In: Proc. British Machine Vision Conf. (BMVC), pp. 949-958.
16. V. Lepetit, and P. Fua Keypoint recognition using randomized trees IEEE Trans. Pattern Anal. Machine Intell. 28 9 2006 1465 1479 (Pubitemid 44132652)
17. Lepetit, V., Lagger, P., Fua, P., 2005. Randomized trees for real-time keypoint recognition. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 775-781.
18. J. Li, and N.M. Allinson A comprehensive review of current local features for computer vision Neurocomputing 71 10-12 2008 1771 1787
19. T. Lindeberg Feature detection with automatic scale selection Int. J. Comput. Vision. 45 2 1998 79 116 (Pubitemid 128517917)
20. D. Lowe Distinctive image features from scale-invariant keypoints Int. J. Comput. Vision. 60 2 2004 91 110
21. Matas, J., Chun, O., Urban, M., Pajdla, T., 2002. Robust wide-baseline stereo from maximally stable extremal regions. In: Proc. British Machine Vision Conf. (BMVC), pp. 384-393.
22. Meltzer, J., Yang, M.-H., Gupta, R., Soatto, S., 2004. Multiple view feature descriptors from image sequences via kernel principal component analysis. In: Proc. European Conf. of Computer Vision (ECCV), pp. 215-227.
23. Miao, Q., Wang, G., Lin, X., Wang, Y., Shi, C., Liao C., 2010. Scale and rotation invariant feature-based object tracking via modified on-line boosting. In: Proc. IEEE Conf. on Image Processing (ICIP) 2010, Hong Kong.
24. Mikolajczyk, K., Schmid, C., 2001. Indexing based on scale invariant interest points. In: Proc. Internat. Conf. on Computer Vision (ICCV), pp. 525-531. (Pubitemid 32794998)
25. Moravec, H., 1977. Towards automatic visual obstacle avoidance. In: Proc. Internat. Joint Conf. on Artificial Intelligence.
26. Nair, V., Clark, J., 2002. An unsupervised, online learning framework for moving object detection. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 317-324.
27. Oza, N., Russell, S., 2001. Experimental comparisons of online and batch versions of bagging and boosting. In: Proc. 7th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining, pp. 359-364.
28. Ta, D.-N., Chen, C., Gelfand, N., pulli, K., 2009. SURFTrac: Efficient tracking and continuous object recognition using local feature descriptors. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2937-2943.
29. K. Tieu, and P. Viola Boosting image retrieval Int. J. Comput. Vision. 56 1-2 2004 17 36
30. T. Tuytelaars, and K. Mikolajczyk Local invariant feature detectors: A survey Found. Trends Comput. Graphics Vision 3 3 2008 177 280
31. T. Tuytelaars, and L. Van Gool Matching widely separated views based on affine invariant region Int. J. Comput. Vision. 59 1 2004 61 85
32. Viola, P., Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 511-518.
33. P. Viola, and M. Jones Robust real-time face detection Int. J. Comput. Vision 57 2 2004 137 154