Linear regression and adaptive appearance models for fast simultaneous modelling and tracking

International Journal of Computer Vision

Volumn 95, Issue 2, 2011, Pages 154-179

  Ellis, Liam ^a,b   Dowson, Nicholas ^c   Matas, Jiri ^d   Bowden, Richard ^b  

^a LINKÖPING UNIVERSITY   (Sweden)

^b UNIVERSITY OF SURREY   (United Kingdom)

^c ROYAL BRISBANE AND WOMEN'S HOSPITAL   (Australia)

^d CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

Author keywords

Online appearance modelling; Regression tracking

Indexed keywords

ADAPTIVE APPEARANCE MODELS; APPEARANCE MODELS; COMPUTATIONALLY EFFICIENT; FAST-TRACKING; LINEAR PREDICTORS; OFF-LINE LEARNING; ONLINE APPEARANCE MODELLING; PERFORMANCE EVALUATION; REGISTRATION TECHNIQUES; REGRESSION FUNCTION; REGRESSION TRACKING; STATE OF THE ART; TARGET LOCATION; TRACKING TECHNIQUES;

FACE RECOGNITION;

REGRESSION ANALYSIS;

EID: 80052654373     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-010-0364-4     Document Type: Article

References (31)

1. S. Avidan 2007 Ensemble tracking IEEE Transactions on Pattern Analysis and Machine Intelligence 29 2 261 271 10.1109/TPAMI.2007.35 (Pubitemid 46420886)
2. S. Baker I Matthews 2004 Lucas-Kanade 20 years on: A unifying framework International Journal of Computer Vision 56 3 221 255 10.1023/B:VISI.0000011205. 11775.fd
3. Bray, M., Kohli, P., & Torr, P. H. S. (2006). Posecut: Simultaneous segmentation and 3d pose estimation of humans using dynamic graph-cuts. In ECCV (2) (pp. 642-655). (Pubitemid 44029761)
4. R. Collins Y. Liu M. Leordeanu 2005 On-line selection of discriminative tracking features IEEE Transaction on Pattern Analysis and Machine Intelligence 27 1 1631 1643 10.1109/TPAMI.2005.205 (Pubitemid 41555407)
5. Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998). Active appearance models. In ECCV (2) (pp. 484-498). (Pubitemid 128093046)
6. Dowson, N., & Bowden, R. (2006). N-tier simultaneous modelling and tracking for arbitrary warps (Vol. II, p. 569).
7. N. Dowson R. Bowden 2008 Mutual information for Lucas-Kanade tracking (milk): An inverse compositional formulation IEEE Transactions on Pattern Analysis and Machine Intelligence 30 1 180 185 10.1109/TPAMI.2007.70757 (Pubitemid 350168365)
8. Ellis, L., Matas, J., & Bowden, R. (2008). Online learning and partitioning of linear displacement predictors for tracking. In BMVC (1) (pp. 33-42).
9. Grabner, H., Grabner, M., & Bischof, H. (2006). Real-time tracking via on-line boosting. In BMVC06 (pp. 47-56). British Machine Vision Association.
10. Grabner, H., Leistner, C., & Bischof, H. (2008). Semi-supervised on-line boosting for robust tracking. In ECCV '08: Proceedings of the 10th European conference on computer vision (pp. 234-247). Berlin: Springer.
11. G. D. Hager P. N. Belhumeur 1998 Efficient region tracking with parametric models of geometry and illumination IEEE Transactions on Pattern Analysis and Machine Intelligence 20 10 1025 1039 10.1109/34.722606 (Pubitemid 128741363)
12. Hansen, B. B., & Morse, B. S. (1999). Multiscale image registration using scale trace correlation. In IEEE computer society conference on computer vision and pattern recognition (Vol. 2, p. 2202).
13. M. Isard A. Blake 1998 Condensation-conditional density propagation for visual tracking International Journal of Computer Vision 29 5 28 10.1023/A:1008078328650 (Pubitemid 128523364)
14. Jepson, A. D., Fleet, D. J., & El-Maraghi, T. F. (2001). Robust online appearance models for visual tracking. In CVPR (1) (pp. 415-422).
15. Jurie, F., & Dhome, M. (2002). Real time robust template matching. In British machine vision conference 2002 (pp. 123-131).
16. Kalal, Z., Matas, J., & Mikolajczyk, K. (2010). P-N learning: bootstrapping binary classifiers by structural constraints. In CVPR.
17. Lucas, B. D., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In IJCAI (pp. 674-679). (Pubitemid 14478744)
18. Marchand, É., Bouthemy, P., Chaumette, F., & Moreau, V. (1999). Robust real-time visual tracking using a 2d-3d model-based approach. In ICCV (pp. 262-268). (Pubitemid 32080049)
19. D. W. Marquardt 1963 An algorithm for least-squares estimation of nonlinear parameters SIAM Journal on Applied Mathematics 11 2 431 441 153071 0112.10505 10.1137/0111030
20. Matas, J., Zimmermann, K., Svoboda, T., & Hilton, A. (2006). Learning efficient linear predictors for motion estimation. In ICVGIP (pp. 445-456).
21. I. Matthews T. Ishikawa S. Baker 2004 The template update problem IEEE Transactions on Pattern Analysis and Machine Intelligence 26 6 810 815 10.1109/TPAMI.2004.16
22. W. W. Mayol D. W. Murray 2008 Tracking with general regression Machine Vision and Applications 19 1 65 72 10.1007/s00138-007-0087-x
23. Ong, E. J., & Bowden, R. (2009). Robust facial feature tracking using selected multi-resolution linear predictors (pp. 1483-1490).
24. D. Paquin D. Levy E. Schreibmann L. Xing 2006 Multiscale image registration Mathematical Biosciences and Engineering 3 2 389 418 2213527 1110.68538 10.3934/mbe.2006.3.389 (Pubitemid 46227023)
25. D. A. Ross J. Lim R. S. Lin M. H. Yang 2008 Incremental learning for robust visual tracking International Journal of Computer Vision 77 1-3 125 141 10.1007/s11263-007-0075-7 (Pubitemid 351294740)
26. Sheikh, Y. A., Khan, E. A., & Kanade, T. (2007). Mode-seeking by medoidshifts. In Eleventh IEEE international conference on computer vision (ICCV 2007) (p. 141).
27. H. Y. Shum R. Szeliski 2000 Systems and experiment paper: Construction of panoramic image mosaics with global and local alignment International Journal of Computer Vision 36 2 101 130 10.1023/A:1008195814169 (Pubitemid 30588074)
28. P. Viola M. Jones 2002 Robust real-time object detection International Journal of Computer Vision 57 2 137 154 10.1023/B:VISI.0000013087.49260.fb
29. Williams, O. M. C., Blake, A., & Cipolla, R. (2003). A sparse probabilistic learning algorithm for real-time tracking. In ICCV (pp. 353-361).
30. Zimmermann, K. (2008). Fast learnable methods for object tracking. PhD thesis, Center for Machine Perception (CMP), Czech Technical University, Prague, Czech Republic.
31. K. Zimmermann J. Matas T. Svoboda 2009 Tracking by an optimal sequence of linear predictors IEEE Transactions on Pattern Analysis and Machine Intelligence 31 4 677 692 10.1109/TPAMI.2008.119