Incremental tensor biased discriminant analysis: A new color-based visual tracking method

Neurocomputing

Volumn 73, Issue 4-6, 2010, Pages 827-839

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

^a XIDIAN UNIVERSITY   (China)

^b ASTON UNIVERSITY   (United Kingdom)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Biased discriminant analysis; Incremental tensor biased discriminant analysis; Tensor biased discriminant analysis; Visual tracking

Indexed keywords

BIASED DISCRIMINANT ANALYSIS; CHROMATIC IMAGES; COLOR BASED TRACKING; COLOR INFORMATION; COLOR TENSORS; IMAGE ENSEMBLE; INCREMENTAL SCHEME; MULTILINEAR ALGEBRA; ONLINE LEARNING; PARTIAL OCCLUSIONS; SPATIAL STRUCTURE; STRUCTURAL RELATIONSHIP; THIRD-ORDER; VISUAL TRACKING;

ALGEBRA; COLOR; TENSORS;

DISCRIMINANT ANALYSIS;

ARTICLE; AUTOMATED PATTERN RECOGNITION; BIASED DISCRIMINANT ANALYSIS; COLOR; COLOR BASED VISUAL TRACKING METHOD; DISCRIMINANT ANALYSIS; ENVIRONMENTAL FACTOR; EXPERIMENTAL STUDY; ILLUMINATION; IMAGE DISPLAY; IMAGE PROCESSING; IMAGE QUALITY; INCREMENTAL TENSOR BIASED DISCRIMINANT ANALYSIS; INTERMETHOD COMPARISON; LEARNING ALGORITHM; ONLINE SYSTEM; PRIORITY JOURNAL; TENSOR BIASED DISCRIMINANT ANALYSIS; VIDEORECORDING;

EID: 75749093321     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.10.013     Document Type: Article

References (35)

1. S. Birchfield, Elliptical head tracking using intensity gradient and color histograms, in: Proceedings of the 1998 IEEE International Conference on Computer Vision and Pattern Recognition, 1998, pp. 232-237.
2. Wang H.Z., Suter D., Schindler K., and Shen C.H. Adaptive object tracking based on an effective appearance filter. IEEE Transactions on Pattern Analysis and Machine Intelligence 29 9 (2007) 1661-1667
3. Hayashi Y., and Fujiyoshi H. Mean-shift-based color tracking in illuminance change. Lecture Notes in Artificial Intelligence 5001 (2008) 302-311
4. M. Isard, A. Blake, Contour tracking by stochastic propagation of conditional density, in: The Fourth European Conference on Computer Vision, LNCX 1064, 1996, pp. 343-356.
5. Allili M.S., and Ziou D. Object tracking in videos using adaptive mixture models and active contours. Neurocomputing 71 10-12 (2008) 2001-2011
6. Cootes T., Edwards G., and Taylor C. Robust real-time periodic motion detection analysis and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 6 (2001) 681-685
7. P. Perez, C. Hue, J. Vermaak, M. Gangnet, Color-based probabilistic tracking, in: The Seventh European Conference on Computer Vision, Lecture Notes in Computer Science, vol. 2350, 2002, pp. 661-675.
8. Comaniciu D., Ramesh V., and Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 5 (2003) 564-577
9. S. Avidan, Ensemble tracking, in: Proceedings of the 2005 IEEE International Conference on Computer Vision and Pattern Recognition, 2005, pp. 494-501.
10. Gross R., Matthews I., and Baker S. Active appearance models with occlusion. Image and Vision Computing 24 (2006) 593-604
11. D. Ross, J. Lim, M.H. Yang, Adaptive probabilistic visual tracking with incremental subspace update, in: The Eighth European Conference on Computer Vision, Lecture Notes in Computer Science, vol. 2350, 2004, pp. 707-720.
12. Lin R.S., Ross D., Lin R.S., and Yang M.H. Adaptive discriminative generative model and its applications. Advances in Neural Information Processing Systems (2004) 801-808
13. C.H. Shen, A.V.D. Hengel, M.J. Brooks, Visual tracking via efficient kernel discriminant subspace learning, in: Proceedings of the 2005 IEEE International Conference on Image Processing, 2005, pp. 590-593.
14. S. Li, M.C. Lee, Fast visual tracking using motion saliency in video, in: Proceedings of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, 2007, pp. 1073-1076.
15. J. Shao, S.H.K. Zhou, R. Chellappa, Appearance-based tracking and recognition using the 3d trilinear tensor, in: Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, 2004, pp. 613-616.
16. X. Li, W.M. Hu, Z.F. Zhang, et al., Robust visual tracking based on incremental tensor subspace learning, in: Proceedings of the 2007 IEEE International Conference on Computer Vision, October 2007, pp. 1-8.
17. J. Wen, X. Gao, X. Li, D. Tao. Incremental learning of weighted tensor subspace for visual tracking, in: Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, USA, October 2009, pp. 3788-3793.
18. Tao D., Li X., Wu X., and Maybank S.J. General tensor discriminant analysis and gabor features for gait recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 29 10 (2007) 1700-1715
19. X. Zhou, T. Huang, Small sample learning during multimedia retrieval using biasmap, in: Proceedings of the 2001 IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, 2001, pp. 11-17.
20. X. Zhou, T. Huang, Comparing discriminate transformations and SVM for learning during multimedia retrieval, in: Proceedings of the ACM Conference on Multimedia, 2001, pp. 137-146.
21. Tao D., Tang X., Li X., and Rui Y. Kernel direct biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm. IEEE Transactions on Multimedia 8 4 (2006) 716-725
22. D. Tao, X. Li, W. Hu, et al., Supervised tensor analysis, in: Proceedings of the 5th IEEE International Conference on Data Mining, 2005, pp. 450-457.
23. Tao D., Song M., Li X., Shen J., et al. Bayesian tensor approach for 3-D face modelling. IEEE Transactions on Circuits and Systems for Video Technology 18 10 (2008) 1397-1410
24. Sun J., Tao D., Papadimitriou S., et al. Incremental tensor analysis: theory and applications. ACM Transactions on Knowledge Discovery in Data 2 3 (2008)
25. D. Tao, J. Sun, J. Shen, et al., Bayesian tensor analysis, in: Proceedings of the IEEE International Joint Conference on Neural Networks, 2008, pp. 1403-1410.
26. D. Tao, J. Sun, J. Shen, et al., Probabilistic tensor analysis with Akaike and Bayesian information criteria, in: Proceedings of the 14th International Conference on Neural Information Processing 2007, Part I, Lecture Notes in Computer Science, vol. 4984, 2008, pp. 791-801.
27. Tao D., Li X., Wu X., and Maybank S. Tensor rank one discriminant analysis-a convergent method for discriminative multilinear subspace selection. Neurocomputing 71 10-12 (2008) 1866-1882
28. D. Tao, X. Li, X. Wu, S. Maybank, Elapsed time in human gait recognition: a new approach, in: Proceedings of the 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, 2006, pp. 177-180.
29. Tao D., Li X., Wu X., and Maybank S. Geometry mean for subspace selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 31 2 (2009) 260-274
30. Gao X., Yang Y., Tao D., and Li X. Discriminative optical flow tensor for video semantic analysis. Computer Vision and Image Understanding 113 3 (2009) 372-383
31. Gao X., Li X., Feng J., and Tao D. Shot-based video retrieval with optical flow tensor and HMM. Pattern Recognition Letters 30 2 (2009) 140-147
32. Yuan Y., Pang Y., Pan J., and Li X. Scene segmentation based on IPCA for visual surveillance. Neurocomputing 72 10-12 (2009) 2450-2454
33. Xu D., Yan S., Tao D., et al. Human gait recognition with matrix representation. IEEE Transactions on Circuits and Systems for Video Technology 16 7 (2006) 896-903
34. Yu H., and Yang J. A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognition 34 (2001) 2067-2070
35. Lu J., Plataniotis K., and Venetsanopoulos A. Face recognition using kernel direct discriminant analysis algorithms. IEEE Transactions on Neural Network 14 1 (2003) 117-126