Hierarchical Kalman-particle filter with adaptation to motion changes for object tracking

Computer Vision and Image Understanding

Volumn 115, Issue 6, 2011, Pages 885-900

  Yin, Shimin ^a   Na, Jin Hee ^a   Choi, Jin Young ^a   Oh, Songhwai ^a  

^a Department of Aerospace Engineering   (South Korea)

Author keywords

Coarse to fine; Hierarchical estimation; Kalman filter; Particle filter

Indexed keywords

ABRUPT MOTION; COARSE-TO-FINE; COMPUTATIONAL LOADS; HIERARCHICAL ESTIMATION; LINEAR MOTION; NON-GAUSSIAN; NON-LINEAR MOTIONS; NON-LINEARITY; OBJECT MOTION; OBJECT TRACKING; PARTICLE FILTER; PROPOSAL DISTRIBUTION; QUANTITATIVE COMPARISON; REAL VIDEO SEQUENCES; SUBSPACE REPRESENTATION; TARGET OBJECT; TRACKING ALGORITHM; TRACKING METHOD; TRACKING PROBLEM;

ALGORITHMS; DISTRIBUTED COMPUTER SYSTEMS; ESTIMATION; KALMAN FILTERS; NONLINEAR FILTERING; TRACKING (POSITION);

TARGET TRACKING;

EID: 79953062027     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2011.02.010     Document Type: Article

References (28)

1. D. Terzopoulos, and R. Szeliski Tracking with Kalman snakes A. Blake, A. Yuille, Active Vision 1992 MIT Press 3 20
2. M.J. Black, A.D. Jepson, EigenTracking: robust matching and tracking of articulated objects using a view-based representation, in: European Conference on Computer Vision, 1996, pp. 329-342. (Pubitemid 126064334)
3. M. Isard, and A. Blake Condensation-conditional density propagation for visual tracking Int. J. Comput. Vis. 29 1 1998 5 28 (Pubitemid 128523364)
4. D. Comaniciu, V. Ramesh, and P. Meer Kernel-based object tracking IEEE Trans. Pattern Anal. Mach. Intell. 25 5 2003 564 577
5. S. Avidan Ensemble tracking IEEE Trans. Pattern Anal. Mach. Intell. 29 2 2007 261 271 (Pubitemid 46420886)
6. M.A. Turk, A.P. Pentland, Face recognition using eigenfaces, in: IEEE Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-591.
7. T.F. Cootes, G.J. Edwards, C.J. Taylor, Active appearance models, in: European Conference on Computer Vision, 1998, pp. 484-498. (Pubitemid 128093046)
8. A. Doucet, N. de Freitas, N.J. Gordon, Sequential Monte Carlo Methods in Practice 2001 Springer-Verlag
9. J. Lim, D. Ross, R. Lin, M. Yang, Incremental learning for visual tracking, in: Neural Information Processing Systems, 2005, pp. 793-800.
10. D. Ross, J. sLim, R. Lin, and M. Yang Incremental learning for robust visual tracking Int. J. Comput Vis. 77 1 2008 125 141 (Pubitemid 351294740)
11. M. Yang, Y. Wu, Tracking non-stationary appearances and dynamic feature selection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 1059-1066.
12. B. Zhang, W. Tian, and Z. Jin Efficient hybrid appearance model for object tracking with occlusion handling Opt. Eng. 46 8 2007
13. C. Chang, and R. Ansari Kernel particle filter for visual tracking IEEE Signal Process. Lett. 12 3 2005 242 245 (Pubitemid 40319400)
14. C. Shan, T. Tan, and Y. Wei Real-time hand tracking using a mean shift embedded particle filter Pattern Recogn. 40 2007 1958 1970 (Pubitemid 46497247)
15. Y. Cheng Mean shift, mode seeking, and clustering IEEE Trans. Pattern Anal. Mach. Intell. 17 8 1995 790 799
16. X. Zhang, W. Hu, S. Maybank, X. Li, M. Zhu, Sequential particle swarm optimization for visual tracking, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1-8.
17. P. Li, T. Zhang, and A. Pece Visual contour tracking based on particle filters Image Vis. Comput. 21 1 2003 111 123
18. Y. Rui, Y. Chen, Better proposal distributions: object tracking using the unscented particle filter, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, 2001, pp. 786-793.
19. Z. Khan, T. Balch, F. Dellaert, A Rao-Blackwellized particle filter for EigenTracking, in: IEEE Conference on Computer Vision and Pattern Recognition, 2004, pp. 980-986.
20. A. Doucet, N. de Freitas, K. Murphy, S. Russell, Rao-Blackwellized particle filtering for dynamic Bayesian networks, in: Uncertainty in Artificial Intelligence, 2000, pp. 176-183.
21. G. Casella, and C. Robert Rao-Blackwellisation of sampling schemes Biometrika 83 1 1996 81 94
22. D. Forsyth, and J. Ponce Computer Vision: A Modern Approach 2002 Prentice Hall
23. M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Trans. Signal Process. 50 2 2002 174 188 (Pubitemid 34291500)
24. M. Tipping, and C. Bishop Probabilistic principal component analysis J. Roy. Stat. Soc., Ser. B 61 3 1999 611 622
25. B. Moghaddam, and A. Pentland Probabilistic visual learning for object representation IEEE Trans. Pattern Anal. Mach. Intell. 19 7 1997 696 710 (Pubitemid 127762262)
26. ∞, and Nonlinear Approaches 2006 Wiley
27. A. Jepson, D. Fleet, and T. El-Maraghi Robust online appearance models for visual tracking IEEE Trans. Pattern Anal. Mach. Intell. 25 10 2003 1296 1311
28. S. Yin, H. Yoo, J. Choi, Enhanced measurement model for subspace-based tracking, in: International Conference on Pattern Recognition, 2010.