Incremental pairwise discriminant analysis based visual tracking

Neurocomputing

Volumn 74, Issue 1-3, 2010, Pages 428-438

  Wen, Jing ^a   Gao, Xinbo ^a   Li, Xuelong ^b   Tao, Dacheng ^c   Li, Jie ^a  

^a XIDIAN UNIVERSITY   (China)

^b XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Incremental learning; Log Euclidean Riemannian; Pairwise discriminant analysis; Visual tracking

Indexed keywords

BACKGROUND INFORMATION; INCREMENTAL LEARNING; LOG-EUCLIDEAN; NONSTATIONARY; OBJECT APPEARANCE; OPTIMAL ADAPTATION; PAIRWISE DISCRIMINANT ANALYSIS; SAMPLE NUMBER; SPATIAL STRUCTURE; TRACKING FAILURE; VISUAL TRACKING;

IMAGE SEGMENTATION;

DISCRIMINANT ANALYSIS;

ARTICLE; ASSOCIATION; BAYESIAN LEARNING; CONTROLLED STUDY; DEPTH PERCEPTION; DISCRIMINANT ANALYSIS; EYE TRACKING; INFORMATION PROCESSING; MATHEMATICAL ANALYSIS; MOVEMENT PERCEPTION; PRIORITY JOURNAL; VISUAL ADAPTATION;

EID: 78649470598     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.07.014     Document Type: Article

References (52)

1. M.J. Black, A.D. Jepson, Eigentracking: obust matching and tracking of articulated objects using view-based representation, in: Proceedings of the Fourth European Conference on Computer Vision, Cambridge, UK, vol. 1, April 15-18, 1996, pp. 329-342.
2. G.D. Hager, P.N. Belhumeur, Real-time tracking of image regions with changes in geometry and illumination, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, vol. 1, June 18-20, 1996, pp. 403-410.
3. S. Birchfield, Elliptical head tracking using intensity gradients and color histograms, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, USA, vol. 1, June 23-25, 1998, pp. 232-237.
4. Levy A., Lindenbaum M. Sequential Karhunen-Loeve basis extraction and its application to images. IEEE Trans. Image Process. 2000, 9(8):1371-1374.
5. A.D. Jepson, D.J. Fleet, T.R. El-Maraghi, Robust online appearance models for visual tracking, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Kauai, HI, vol. 1, December 9-14, 2001, pp. 415-422.
6. K. Toyama, A. Blake, Probabilistic tracking in a metric space, in: Proceedings of the IEEE International Conference on Computer Vision, Vancouver, BC, Canada, vol. 2, July 7-14, 2001, pp. 50-57.
7. J. Vermaak, P. Perez, M. Gangnet, A. Blake, Towards improved observation models for visual tracking: selective adaptation, in: Proceedings of the Seventh European Conference on Computer Vision, Copenhagen, Denmark, vol. 1, May 2002, pp. 645-660.
8. R.T. Collins, Y. Liu, On-line selection of discriminative tracking features, in: Proceedings of the IEEE International Conference on Computer Vision, Nice, France, vol. 1, October 13-16, 2003, pp. 346-352.
9. K.C. Lee, J. Ho, M.H. Yang, D. Kriegman, Video-based face recognition using probabilistic appearance manifolds, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Madison, WI, vol. 1, June 18-20, 2003, pp. 313-320.
10. J. Ho, K.C. Lee, M.H. Yang, D.J. Kriegman, Visual tracking using learned linear subspace, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, June 27-July 2, 2004, pp. 782-789.
11. D. Ross, J. Lim, M.H. Yang, Adaptive probabilistic visual tracking with incremental subspace update, in: Proceedings of the European Conference on Computer Vision, Prague, Czech Republic, vol. 1, May 2004, pp. 215-227.
12. Avidan S. Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26(8):1064-1072.
13. J. Ho, K.C. Lee, M.H. Yang, D.J. Kriegman, Visual tracking using learned linear subspace, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Grand Hyatt, Washington, vol. 1, June 27-July 2, 2004, pp. 782-789.
14. J. Lim, D. Ross, R.-S. Lin, M.-H. Yang, Incremental learning for visual tracking, in: Proceedings of the 17th Advances in Neural Information Processing Systems, Vancouver, BC, Canada, December 13-18, 2004, pp. 801-808.
15. Zhou S.K., Chellappa R., Moghaddam B. Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Trans. Image Process. 2004, 13(11):1491-1506.
16. A. Elgammal, Learning to track: conceptual manifold map for closed-form tracking, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, vol. 1, June 20-26, 2005, pp. 724-730.
17. J. Wang, X. Chen, W. Gao, Online selecting discriminative tracking features using particle filter, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, vol. 2, June 20-26, 2005, pp. 1037-1042.
18. S. Avidan, Ensemble tracking, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, vol. 2, June 20-25, 2005, pp. 494-501.
19. A.P. Leung S. Gong, Online feature selection using mutual information for real-time multi-view object tracking, in: Proceedings of the IEEE International Workshop Analysis and Modeling of Faces and Gestures, Beijing, China, October 16, 2005, pp. 184-197.
20. F. Tang, H. Tao, Object tracking with dynamic feature graph, in: Proceedings of the IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, Beijing, China, October 15-16, 2005, pp. 25-32.
21. M. Yang, Y. Wu, Tracking non-stationary appearances and dynamic feature selection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, vol. 2, June 20-26, 2005, pp. 1059-1066.
22. K.C. Lee, D.J. Kriegman, Online learning of probabilistic appearance manifolds for video-based recognition and tracking, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, vol. 1, June 20-25, 2005, pp. 852-859.
23. X. He, D. Cai, S. Yan, H. Zhang, Neighborhood preserving embedding, in: Proceedings of the IEEE Conference on Computer Vision, Beijing, China, vol. 2, 2005, pp. 1208-1213.
24. H. Grabner, M. Grabner, H. Bischof, Real-time tracking via on-line boosting, in: Proceedings of the Conference on British Machine Vision, Edinburgh, vol. 1, September 4-7, 2006, pp. 47-56.
25. H. Lim, V.I. Morariu, O.I. Camps, M. Sznaier, Dynamic appearance modeling for human tracking, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, vol. 1, June 17-22, 2006, pp. 751-757.
26. Arsigny V., Fillard P., Pennec X., Ayache N. Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM J. Matrix Anal. Appl. 2007, 29(1):328-347.
27. O. Tuzel, F. Porikli, P. Meer, Human detection via classification on Riemannian manifolds, in: Proceedings of the Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, June 17-22, 2007, pp. 1-8.
28. Z. Yin, R. Collins, On-the-fly object modeling while tracking, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, vol. 1, June 17-22, 2007, pp. 1-8.
29. D. Cai, X. He, J. Han, Efficient kernel discriminant analysis via spectral regression, in: Proceedings of the International Conference on Data Mining, Omaha, Nebraska, USA, October 28-31, 2007, pp. 427-432.
30. D. Cai, X. He, J. Han, Semi-supervised discriminant analysis, in: Proceedings of the International Conference on Computer Vision, Rio de Janeiro, Brazil, October 14-20, 2007, pp. 1-7.
31. Xu D., Lin S., Yan S., Tang X. Rank-one projections with adaptive margin for face recognition. IEEE Trans. Syst. Man Cybern. Part B 2007, 37(5):1226-1236.
32. X. Li, W. Hu, Z. Zhang et al., Visual tracking via incremental log-Euclidean Riemannian subspace learning, in: Proceedings of the Conference on Computer Vision and Pattern Recognition, Anchorage, AK, vol. 1, June 23-28, 2008, pp. 1-8.
33. Cai D., He X., Han J. SRDA: an efficient algorithm for large-scale discriminant analysis. IEEE Trans. Knowledge Data Eng. 2008, 20(1):1-12.
34. Y. Yuan, Y. Pang, Discriminant adaptive edge weights for graph embedding, in: Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing, Las Vegas, NV, USA, 2008, pp. 1993-1996.
35. Y. Yuan, Y. Pang, Boosting simple projections for multi-class dimensionality reduction, in: Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, Singapore, 2008, pp. 2231-2235.
36. He X., Cai D., Han J. Learning a maximum margin subspace for image retrieval. IEEE Trans. Knowledge Data Eng. 2008, 20(2):189-201.
37. Li X., Lin S., Yan S., Xu D. Discriminant locally linear embedding with high-order tensor data. IEEE Trans. Syst. Man Cybern. Part B 2008, 38(2):342-352.
38. Xu D., Yan S., Lin S., Huang T.S. Convergent 2-D subspace learning with null space analysis. IEEE Trans. Circuits Syst. Video Technol. 2008, 18(12):1753-1759.
39. Xu D., Yan S., Zhang L., Zhang H., Huang T.S. Reconstruction and recognition of tensor-based objects with concurrent subspaces analysis. IEEE Trans. Circuits Syst. Video Technol. 2008, 18(1):36-47.
40. Xu D., Yan S., Lin S., Huang T.S., Chang S.F. Enhancing bilinear subspace learning by element rearrangement. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31(10):1913-1920.
41. Xu D., Yan S. Semi-supervised bilinear subspace learning. IEEE Trans. Image Process. 2009, 18(7):1671-1676.
42. Yuan Y., Pang Y., Pan J., Li X. Scene segmentation based on IPCA for visual surveillance. Neurocomputing 2009, 72(10-12):2450-2454.
43. Yuan Y., Li X., Pang Y., Lu X., Tao D. Binary sparse nonnegative matrix factorization. IEEE Trans. Circuits Syst. Video Technol. 2009, 19(5):772-777.
44. Lu Y., Tian Q. Discriminant subspace analysis: an adaptive approach for image classification. IEEE Trans. Multimedia 2009, 11(7):1289-1300.
45. Zhou H., Yuan Y., Shi C. Object tracking using SIFT features and mean shift. Comput. Vision Image Understanding 2009, 113(2):345-352.
46. Zhou H., Yuan Y., Zhang Y., Shi C. Non-rigid object tracking in complex scenes. Pattern Recognition Lett. 2009, 30(2):98-102.
47. Zhang T., Fang B., Tang Y., Shang Z., Xu B. Generalized discriminant analysis: a matrix exponential approach. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2010, 40(1):186-197.
48. Li X., Pang Y. Deterministic column-based matrix decomposition. IEEE Trans. Knowledge Data Eng. 2010, 22(1):145-149.
49. He X. Laplacian regularized d-optimal design for active learning and its application to image retrieval. IEEE Trans. Image Process. 2010, 19(1):254-263.
50. Yuan Y., Pang Y., Li X. Footwear for gender recognition. IEEE Trans. Circuits Syst. Video Technol. 2010, 20(1):131-135.
51. Wen J., Gao X., Yuan Y., Tao D. Incremental tensor biased discriminant analysis: a new color based visual tracking. NeuroComputing 2010, 73(4-6):827-839.
52. X. Li, Y. Pang, Y. Yuan, L1-norm-based 2DPCA, IEEE Trans. Syst. Man Cybern. Part B 40 (4) (2010) 1170-1175.