Real-time object tracking using bounded irregular pyramids

Pattern Recognition Letters

Volumn 28, Issue 9, 2007, Pages 985-1001

  Marfil, R ^a   Molina Tanco, L ^a   Rodriguez J A ^a   Sandoval, F ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Hierarchical template matching; Multiple object tracking; Non rigid object tracking; Target representation and localization; Template based tracking

Indexed keywords

COMPUTATIONAL METHODS; HIERARCHICAL SYSTEMS; LEARNING SYSTEMS; PATTERN MATCHING; REAL TIME SYSTEMS; TRACKING (POSITION);

HIERARCHICAL TEMPLATE MATCHING; MULTIPLE OBJECT TRACKING; NON-RIGID OBJECT TRACKING; TARGET REPRESENTATION AND LOCALIZATION; TEMPLATE-BASED TRACKING;

OBJECT RECOGNITION;

EID: 34147183316     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2006.11.013     Document Type: Article

References (40)

1. Althof R., Wind M., and Dobbins J. A rapid and automatic image registration algorithm with subpixel accuracy. IEEE Trans. Med. Imag. 16 (1997) 308-316
2. Bertolino, P., Montanvert, A., 1996. Multiresolution segmentation using the irregular pyramid. In: Int. Conf. on Image Processing, vol. 1, pp. 257-260.
3. Blake A., Isard M., and Reynard D. Learning to track the visual motion of contours. Art. Intell. 78 (1995) 101-134
4. Brun L., and Kropatsch W. Construction of combinatorial pyramids. In: Hancock E., and Vento M. (Eds). Graph based Representations in Pattern Recognition. LNCS 2726 (2003), Springer Verlag 1-12
5. Cavallaro A., Steiger O., and Ebrahimi T. Tracking video objects in cluttered background. IEEE Trans. Circuits Syst. Video Technol. 15 4 (2005) 575-584
6. Comaniciu D., Ramesh V., and Meer P. Kernel-based object tracking. IEEE Trans. Pattern Anal. Machine Intell. 25 5 (2003) 564-577
7. Dani P., and Chaudhuri S. Automated assembling of images: Image montage preparation. Pattern Recognition 28 (1995) 431-445
8. Elgammal A., Duraiswami R., Harwood D., and Davis L. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90 7 (2002) 1151-1163
9. Feris, R., Krüger, V., Cesar Jr., R., 2001. Efficient real-time face tracking in wavelet subspace. In: Proc. Internat. Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-Time Systems, Vancouver, BC, Canada.
10. Hang, B., Yang., C., Duraiwami, R., Davis, L., 2005. Bayesian filtering and integral image for visual tracking. In: Proc. 6th Internat. Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2005).
11. Ho, J., Lee, K., Yang, M.-H., Kriegman, D., 2004. Visual tracking using learned linear subspaces. In: Proc. Internat. Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 782-789.
12. Huart, J., Bertolino, P., 2005. Similarity-based and perception-based image segmentation. In: IEEE Internat. Conf. Image Process. (ICIP2005), vol. 3, pp. 1148-1151.
13. Irani M., Rousso B., and Peleg S. Computing occluding and transparent motions. Internat. J. Comput. Vision 12 1 (1994) 5-16
14. Jepson A., Fleet D., and El-Maraghi T. Robust online appearance models for visual tracking. IEEE Trans. Pattern Anal. Machine Intell. 25 10 (2003) 1296-1311
15. Koller D., Danilidis K., and Nagel H. Model-based object tracking in monocular image sequences of road traffic scenes. Internat. J. Comput. Vision 10 3 (1993) 257-281
16. Kropatsch W., and Haxhimusa Y. Grouping and segmentation in a hierarchy of graphs. In: Bouman C.A., and Miller E.L. (Eds). Computational Imaging II (2004), SPIE 193-204
17. Krüger, V., Sommer, G., 2000. Affine real-time face tracking using Gabor wavelet networks. In: Proc. Internat. Conf. on Pattern Recognition, pp. 3-8.
18. Kumar, R., Sawhney, H., Asmuth, J., Pope, A., Hsu, S., 1998. Registration of video to geo-referenced imagery. In: Proc. Internat. Conf. on Pattern Recognition (ICPR98), pp. 1393-1399.
19. Lallich S., Muhlenbach F., and Jolion J. A test to control a region growing process within a hierarchical graph. Pattern Recognition 36 (2003) 2201-2211
20. Liu T., and Chen H. Real-time tracking using trust-region methods. IEEE Trans. Pattern Anal. Machine Intell. 26 3 (2003) 397-402
21. Marfil R., Rodriguez J., Bandera A., and Sandoval F. Bounded irregular pyramid: A new structure for colour image segmentation. Pattern Recognition 37 3 (2004) 623-626
22. Marfil, R., Bandera, A., Rodriguez, J., Sandoval, F., 2004b. Real-time template-based tracking of non-rigid objects using bounded irregular pyramids. In: Proc. IEEE/RSJ Internat. Conf. on Intelligent Robots Systems, vol. 1, pp. 301-306.
23. Martin, J., Devin, V., Crowley, J., 1998. Active hand tracking. In: Proc. 3rd IEEE Internat. Conf. on Automatic Face and Gesture Recognition, pp. 573-578.
24. Molina-Tanco, L., Bandera, J., Marfil, R., Sandoval, F., 2005. Real-time human motion analysis for human-robot interaction. In: IEEE/RSJ Internat. Conf. on Intelligent Robots and Systems (IROS05), pp. 1402-1407.
25. Nguyen, H., Smeulders, A., 2002. Template tracking using color invariant pixel features. In: Proc. Internat. Conf. on Image Processing (ICIP02), pp. 569-573.
26. Nguyen H., and Smeulders A. Fast occluded object tracking by a robust appearance filter. IEEE Trans. Pattern Anal. Machine Intell. 26 8 (2004) 1099-1104
27. Nguyen, H., Worring, M., van den Boomgaard, R., 2001. Occlusion robust adaptive template tracking. In: Proc. IEEE Conf. on Computer Vision (ICCV01), vol. 1, pp. 678-683.
28. Nummiaro, K., Koller-Meier, E., Roth, D., Van Gool, L., 2003. Color-based object tracking in multi-camera environments. In: Proc. 25th Pattern Recognition Symposium (DAGM'03), pp. 591-599.
29. Raja Y., Mckenna S., and Gong S. Tracking color objects using adaptive mixture models. Image Vision Comput. 17 (1999) 225-231
30. Rosenfeld A., and Vanderbrug G. Coarse-fine template matching. IEEE Trans. Systems Man Cybernet. 7 (1977) 104-107
31. Rucklidge, W., 1997. Efficient guaranteed search for gray-level patterns. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 717-723.
32. Scott D. Multivariate Density Estimation (1992), Wiley
33. Shi, J., Tomasi, C., 1994. Good features to track. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 593-600.
34. Shinagawa Y., and Kunii T. Unconstrained automatic image matching using multiresolutional critical-point filters. IEEE Trans. Pattern Anal. Machine Intell. 20 (1998) 994-1010
35. Sidenbladh, H., Black, M., Fleet, D., 2000. Stochastic tracking of 3d human figures using 2d image motion. In: Proc. European Conf. on Computer Vision, vol. 2, pp. 702-718.
36. Tao, H., Sawhney, H., Kumar, R., 2000. Dynamic layer representation with applications to tracking. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 134-141.
37. Tao H., Sawhney H., and Kumar R. Object tracking with bayesian estimation of dynamic layer representations. IEEE Trans. Pattern Anal. Machine Intell. 24 1 (2002) 75-89
38. Thévenaz P., Ruttimann U., and Unser M. A pyramidal approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7 (1998) 27-41
39. Vanderbrug G., and Rosenfeld A. Two stage template matching. IEEE Trans. Comput. 26 (1977) 384-393
40. Wong R., and Hall E. Sequential hierarchical scene matching. IEEE Trans. Comput. 27 (1978) 359-366