Abstract

Tracking moving objects in a video is of critical importance in various fields such as traffic monitoring, video surveillance, human motion capture, etc. However, tracking multiple objects in a video is very challenging. To meet that, authors proposed a new adaptive technique for object localization through local and global appearance of target. Local layer concentrates on the target's geometric deformation, i. e. the target structure is updated through adding and removing the local patches. The deformation information is constrained through the global layer, which concentrates on the shape, appearance, and color. The deformation information is passed from local to global layer through particle filter initialization and Hidden Markov Model (HMM). Particle filter is used to detect the local layer patches, and the sequence of deformation information is stored using HMM at global layer. This enhancement to the global layer improves multiple objects tracking efficiency. The efficiency of the proposed technique is evaluated through experimenting with a video containing multiple moving objects. Result analysis shows that the proposed method efficiently tracks multiple moving objects in the video.

References

• BROIDA, T. AND CHELLAPPA, R. 1986. Estimation of object motion parameters from noisy images. IEEE Trans. Patt. Analy. Mach. Intell. 8, 1, 90–99.
• BAR-SHALOM, Y. AND FOREMAN, T. 1988. Tracking and Data Association. Academic Press Inc.
• SALARI, V. AND SETHI, I. K. 1990. Feature point correspondence in the presence of occlusion. IEEE Trans. Patt. Analy. Mach. Intell. 12, 1, 87–91.
• HUTTENLOCHER, D. , NOH, J. , AND RUCKLIDGE, W. 1993. Tracking nonrigid objects in complex scenes. In IEEE International Conference on Computer Vision (ICCV). 93–101.
• STREIT, R. L. AND LUGINBUHL, T. E. 1994. Maximum likelihood method for probabilistic multi-hypothesis tracking. In Proceedings of the International Society for Optical Engineering (SPIE. ) vol. 2235. 394–405.
• SHI, J. AND TOMASI, C. 1994. Good features to track. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 593–600.
• RONFARD, R. 1994. Region based strategies for active contour models. Int. J. Comput. Vision 13, 2, 229–251.
• CASELLES, V. , KIMMEL, R. , AND SAPIRO, G. 1995. Geodesic active contours. In IEEE International Conference on Computer Vision (ICCV). 694–699.
• BLACK, M. AND JEPSON, A. 1998. Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. Int. J. Comput. Vision 26, 1, 63–84.
• BLAKE, A. AND ISARD,M. 2000. Active Contours:The Application ofTechniques from Graphics,Vision, Control Theory and Statistics to Visual Tracking of Shapes in Motion. Springer.
• BERTALMIO, M. , SAPIRO, G. , AND RANDALL, G. 2000. Morphing active contours. IEEE Trans. Patt. Analy. Mach. Intell. 22, 7, 733–737.
• VEENMAN, C. , REINDERS, M. , AND BACKER, E. 2001. Resolving motion correspondence for densely moving points. IEEE Trans. Patt. Analy. Mach. Intell. 23, 1, 54–72.
• AVIDAN, S. 2001. Support vector tracking. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 184–191.
• COMANICIU, D. 2002. Bayesian kernel tracking. In Annual Conference of the German Society for Pattern Recognition. 438–445.
• KANG, J. , COHEN, I. , AND MEDIONI, G. 2004. Object reacquisition using geometric invariant appearance model. In International Conference on Pattern Recognition. 759-762.