Adaptive multi-cue tracking by online appearance learning

Neurocomputing

Volumn 74, Issue 6, 2011, Pages 1035-1045

  Wang, Qing ^a   Chen, Feng ^a   Xu, Wenli ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Appearance modeling; Appearance variations; Target representation; Weight update

Indexed keywords

APPEARANCE MODELING; APPEARANCE MODELS; APPEARANCE VARIATIONS; BAYESIAN INFERENCE; MATRIX; OBJECT TRACKING; OBSERVATION MODEL; SPATIAL CORRELATIONS; STATE-OF-THE-ART METHODS; SUBSPACE ANALYSIS; TARGET AND BACKGROUND; TARGET OBJECT; TARGET REGIONS; TARGET REPRESENTATION; TRACKING ALGORITHM; TRACKING PERFORMANCE; WEIGHT UPDATE;

BAYESIAN NETWORKS; INFERENCE ENGINES; LEARNING ALGORITHMS;

TARGET TRACKING;

ARTICLE; BAYESIAN LEARNING; CONTROLLED STUDY; EYE TRACKING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MATHEMATICAL MODEL; ONLINE SYSTEM; PRIORITY JOURNAL;

EID: 79751526258     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.11.020     Document Type: Article

References (32)

1. S. Birchfield, Elliptical head tracking using intensity gradients and color histograms, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998, pp. 232-237.
2. Triesch J., von der Malsburg C. Democratic integration: self-organized integration of adaptive cues. Neural Computation 2001, 13(9):2049-2074.
3. Nickels K., Hutchinson S. Estimating uncertainty in ssd-based feature tracking. Image Vision Computing 2002, 20(1):47-58.
4. E. Maggio, F. Smeraldi, A. Cavallaro, Combining colour and orientation for adaptive particle filter-based tracking, in: Proceedings of the British Machine Vision Conference, 2005, pp. 659-668.
5. Ojala T., Pietikäinen M., Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 1996, 29(1):51-59.
6. Isard M., Blake A. Condensation-conditional density propagation for visual tracking. International Journal of Computer Vision 1998, 29(1):5-28.
7. Comaniciu D., Ramesh V., Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(5):564-575.
8. P. Pérez, C. Hue, J. Vermaak, M. Gangnet, Color-based probabilistic tracking, in: Proceedings of the European Conference on Computer Vision-part I, 2002, pp. 661-675.
9. S.T. Birchfield, S. Rangarajan, Spatiograms versus histograms for region-based tracking, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2005, pp. 1158-1163.
10. W. Lu, J. Little, Simultaneous tracking and action recognition using the pca-hog descriptor, in: Proceedings of the Third Canadian Conference on Computer and Robot Vision, 2006.
11. Zhou H., Yuan Y., Shi C. Object tracking using sift features and mean shift. Computer Vision and Image Understanding 2009, 113(3):345-352.
12. F. Porikli, O. Tuzel, P. Meer, Covariance tracking using model update based on lie algebra, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2006, pp. 728-735.
13. Avidan S. Support vector tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(8):1064-1072.
14. M.J. Black, Eigentracking: robust matching and tracking of articulated objects using a view-based representation, in: Proceedings of the European Conference on Computer Vision, 1996, pp. 329-342.
15. A.D. Jepson, D.J. Fleet, T.R. El-Maraghi, Robust online appearance models for visual tracking, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2001, pp. I-415-I-422.
16. J. Ho, K.-C. Lee, M.-H. Yang, D. Kriegman, Visual tracking using learned linear subspaces, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2004, pp. I-782-I-789.
17. K.-C. Lee, D. Kriegman, Online learning of probabilistic appearance manifolds for video-based recognition and tracking, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2005, pp. 852-859.
18. Ross D., Lim J., Lin R.-S., Yang M.-H. Incremental learning for robust visual tracking. International Journal of Computer Vision 2008, 77(1-3):125-141.
19. X. Li, W. Hu, Z. Zhang, X. Zhang, M. Zhu, J. Cheng, Visual tracking via incremental log-Euclidean Riemannian subspace learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
20. Collins R.T., Liu Y., Leordeanu M. Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005, 27(10):1631-1643.
21. Avidan S. Ensemble tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007, 29(2):261-271.
22. T. Parag, F. Porikli, A. Elgammal, Boosting adaptive linear weak classifiers for online learning and tracking, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
23. B. Babenko, M.-H. Yang, S. Belongie, Visual tracking with online multiple instance learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 983-990.
24. Ojala T., Pietikainen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24(7):971-987.
25. de Lathauwer L., de Moor B., Vandewalle J. On the best rank-1 and rank-(r1,r2,, rn) approximation of higherorder tensors. SIAM Journal of Matrix Analysis and Applications 2000, 21(4):1324-1342.
26. D. Ross, J. Lim, M.-H. Yang, Adaptive probabilistic visual tracking with incremental subspace update, in: Proceedings of the European Conference on Computer Vision, 2004, pp. 470-482.
27. Lu H., Plataniotis K., Venetsanopoulos A. Mpca: multilinear principal component analysis of tensor objects. IEEE Transactions on Neural Networks 2008, 19(1):18-39.
28. Levy A., Lindenbaum M. Sequential Karhunen-Loeve basis extraction and its application to images. IEEE Transactions on Image Processing 2000, 9(8):1371-1374.
29. M. Brand, Incremental singular value decomposition of uncertain data with missing values, in: Proceedings of the European Conference on Computer Vision, 2002, pp. 707-720.
30. Ec funded caviar project/ist 2001 37540 http://homepages.inf.ed.ac.uk/rbf/caviar/.
31. Jepson A.D., Fleet D.J., El-Maraghi T.F. Robust online appearance models for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(10):1296-1311.
32. A. Adam, E. Rivlin, I. Shimshoni, Robust fragments-based tracking using the integral histogram, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 2006, pp. 798-805.