MCMC-based feature-guided particle filtering for tracking moving objects from a moving platform

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Volumn , Issue , 2009, Pages 828-833

  Lin, Chung Ching ^a   Wolf, Wayne ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FOREGROUND SEGMENTATION; MARKOV CHAIN MONTE CARLO; MOTION PROBLEMS; MOVING OBJECTS; MOVING PLATFORM; OBJECT MOTION; PARTICLE FILTER; PARTICLE FILTERING; PARTICLE FILTERING ALGORITHMS; RECURSIVE BAYESIAN ESTIMATION; ROBUST TRACKING; TRACKING ALGORITHM; TRACKING FAILURE; TRACKING MOVING OBJECTS; TRACKING PERFORMANCE;

BAYESIAN NETWORKS; CAMERAS; COMPUTER VISION; MARKOV PROCESSES; MONTE CARLO METHODS; NONLINEAR FILTERING; TARGET TRACKING;

TRACKING (POSITION);

EID: 77953190196     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2009.5457616     Document Type: Conference Paper

References (13)

1. A. Behrad, A. Shahrokni, S. Motamedi, and K. Madani. A robust vision-based moving target detection and tracking system. In Proceedings of Image and Vision Computing Conference, 2001.
2. B. Bethke, M. Valenti, J. How, and J. Vian. Cooperative Vision Based Estimation and Tracking Using Multiple UAVs. Lecture Notes in Control and Information Sciences, 369:179, 2007.
3. Y. Cao, P. Cook, and A. Renfrew. Vehicle Ego-Motion Estimation by using Pulse-Coupled Neural Network. In Proceedings of the International Machine Vision and Image Processing Conference, 2007. IMVIP 2007., pages 185-191, 2007.
4. W. Chang, C. Chen, and Y. Hung. Appearance-guided particle filtering for articulated hand tracking. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, volume 1, 2005.
5. M. Fischler and R. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. 1981.
6. M. Gleicher and F. Liu. Re-cinematography: improving the camera dynamics of casual video. In Proceedings of the 15th international conference on Multimedia, pages 27-36. ACM New York, NY, USA, 2007.
7. S. Greenhill and S. Venkatesh. Virtual observers in a mobile surveillance system. In MULTIMEDIA '06: Proceedings of the 14th annual ACM international conference on Multimedia, pages 579-588, New York, NY, USA, 2006. ACM.
8. Y. Jian, W. Chang, and C. Chen. Attractor-guided particle filtering for lip contour tracking. Lecture Notes in Computer Science, 3851:653, 2006.
9. Z. Khan, T. Balch, and F. Dellaert. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11):1805-1819, 2005.
10. P. Kumar, A. Dick, and M. Brooks. Integrated Bayesian multi-cue tracker for objects observed from moving cameras. In Image and Vision Computing New Zealand, 2008. IVCNZ 2008. 23rd International Conference, pages 1-6, 2008.
11. M. Meuter, U. Iurgel, S. Park, and A. Kummert. The unscented Kalman filter for pedestrian tracking from a moving host. In 2008 IEEE Intelligent Vehicles Symposium, pages 37-42, 2008.
12. M. van Leeuwen. Motion estimation and interpretation for in-car systems. Ph.D. dissertatioin, University of Amsterdam, 2002.
13. A. Zisserman. Matlab functions for multiple view geometry. http://www.robots.ox.ac.uk/~vgg/hzbook/code/.