Towards robust multi-cue integration for visual tracking

Machine Vision and Applications

Volumn 14, Issue 1, 2003, Pages 50-58

  Spengler, Martin ^a   Schiele, Bernt ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

Condensation; Cue integration; Self organization; Tracking

Indexed keywords

ALGORITHMS; MATHEMATICAL MODELS; PROBABILITY DENSITY FUNCTION; SENSOR DATA FUSION; SENSORS; SET THEORY;

CONDENSATION ALGORITHM; CUE INTEGRATION; SELF-ORGANIZATION; VISUAL TRACKING;

COMPUTER VISION;

EID: 4544326145     PISSN: 09328092     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00138-002-0095-9     Document Type: Article

References (17)

1. Bishop CM (1999) Neural networks for pattern recognition. Oxford University Press, Oxford
2. Blake A, Isard M (1998) Active contours: The application of techniques from graphics, vision, control theory and statistics to visual tracking of shapes in motion. Berlin Heidelberg New York, Springer
3. Bräutigam C (1998) A model-free voting approach to cue integration. PhD thesis, Dept. of Numerical Analysis and Computing Science, KTH (Royal Institute of Technology, Stockholm, Sweden)
4. Clark J, Yuille A (1994) Data fusion for sensory information processing. Kluwer, Boston
5. Crowley J, Berard F (1997) Multi-modal tracking of faces for video communications. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, Los Alamitos, CA
6. Dempster A, Laird N, Rubin D (1997) Maximum likelihood from incomplete data via the em algorithm. J Royal Stat Soc Series B 39:1-38
7. Gonzalez RC, Woods RE (1993) Digital image processing. Addison-Wesley, New York
8. Grenander U, Chow Y, Keenan M (1991) HANDS: A Pattern theoretic study of biological shapes. Berlin Heidelberg New York, Springer
9. Isard M, Blake A (1998) Condensation - conditional density propagation for visual tracking. Int J Comput Vision 29(1):5-28
10. Isard M, Blake A (1998) Icondensation: Unifying low-level and high-level tracking in a stochastic framework. In: Proceedings of the 5th European Conference on Computer Vision (ECCV'98), vol I, pp 893-908. Berlin Heidelberg New York, Springer
11. Kragić D, Christensen HI (1999) Integration of visual cues for active tracking of an end-effector. In: IROS'99, vol 1, October, pp 362-368. IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE Press, Piscataway, NJ
12. Parhami B (1994) Voting algorithms. IEEE Trans Reliability 43(3):617-629
13. Toyama K, Hager G (1999) Incremental focus of attention for robust vision-based tracking. Int J Comput Vision 35(1): 45-63
14. Toyama K, Hager G (1996) Incremental focus of attention for robust visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, Los Alamitos, CA
15. Triesch J (2000) Self-organized integration of adaptive visual cues for face tracking. In: Dasarathy (ed) Sensor fusion: Architectures, algorithms, and applications IV, SPIE 4051:397-406
16. Triesch J, Ballard D, Jacobs R (2000) Fast temproal dynamics of visual cue integration. Technical report, University of Rochester, Computer Science Department, September
17. Triesch J, von der Malsburg C (2000) Self-organized integration of adaptive visual cues for face tracking. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition. IEEE Computer Society, Los Alamitos, CA