Real-time pose estimation of articulated objects using low-level motion

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Daubney, Ben ^a   Gibson, David ^a   Campbell, Neill ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHLORINE COMPOUNDS; COMPUTER VISION; ESTIMATION; FEATURE EXTRACTION; IMAGE PROCESSING; MOTION ESTIMATION; STANDARDS; SYSTEMATIC ERRORS;

A-PRIORI; ARTICULATED OBJECTS; BOTTOM-UP APPROACH; CLOUD OF POINTS; COMPLEX SCENES; DATA CONTAINS; FEATURE TRACKER; GAUSSIAN; GENERATIVE MODELING; MISSING DATA; MOTION MODELING; OBSERVATIONAL DATA; PICTORIAL STRUCTURES; POSE-ESTIMATION; PROBABILISTIC FRAMEWORKS; TRACKING ERRORS;

PATTERN RECOGNITION;

EID: 51949100475     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587530     Document Type: Conference Paper

References (22)

1. A. Agarwal and B. Triggs. Tracking articualted motion with piecewise learned dynamical models. In ECCV, pages 54-65, 2004. 1
2. A. F. Bobick and J. W. Davis. The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(3):257-267, 2001.
3. J. Cutting and L. Kozlowski. Recognizing friends by their walk: Gait perception without familiarity cues. Bulletin Psychonometric Society, 9:353-356, 1977.
4. J. Deutscher, A. Blake, and I. D. Reid. Articulated body motion capture by annealed particle filtering. In CVPR, pages 126-133, 2000.
5. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In IEEE international workshop on: Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pages 65-72, 2005.
6. A. Fathi and G. Mori. Human pose estimation using motion exemplars. In ICCV, pages 1-8, 2007.
7. P. Felzenszwalb and D. Huttenlocher. Distance transforms of sampled functions. In Cornell Computing and Information Science Technical Report TR2004-1963, 2004.
8. P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. IJCV, pages 55-79, 2005.
9. M. Fischler and R. Elsclager. The representation and matching of pictorial structures. In IEEE Transactions on Computer, 22(1), pages 67-92, 1973.
10. A. Fossati, M. Dimitrijevic, V. Lepetit, and P. Fua. Bridging the gap between detection and tracking for 3d monocular video-based motion capture. In CVPR, pages 1-8, 2007.
11. G. Johansson. Visual perception of biological motion and a model for its analysis. Perception and Psychophysics, 14:201-211, 1973.
12. S. Ju, M. Black, and Y. Yacoob. Cardboard people: A parameterized model of articulated motion. In International Conference on Automatic Face and Gesture Recognition, pages 38-44, 1996.
13. X. Lan and D. P. Huttenlocher. A unified spatio-temporal articulated model for tracking. In CVPR, pages 722-729, 2004.
14. M. W. Lee and R. Nevatia. Human pose tracking using multilevel structured models. In ECCV, pages 368-381, 2006.
15. D. Ramanan and D. Forsyth. Finding and tracking people from the bottom up. In CVPR, pages 467-474, 2003.
16. J. Rittscher, A. Blake, A. Hoogs, and G. Stein. Mathematical modelling of animate and intentional motion. In Philos Trans R Soc Lond B Biol Sci. 2003 March 29; 358(1431): 475490.
17. C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: A local svm approach. In ICPR, pages 32-36, 2004.
18. J. Shi and C. Tomasi. Good features to track. In CVPR, pages 593-600, 1994.
19. L. Sigal, B. Sidharth, S. Roth, M. Black, and M. Isard. Tracking loose-limbed people. In CVPR, pages 421-428, 2004.
20. Y. Song, L. Goncalves, and P. Perona. Learning probabilistic structure for human motion detection. In CVPR, pages 771-777, 2001.
21. L. Torresani and C. Bregler. Space-time tracking. In ECCV, pages 801-812, 2002.
22. R. Urtasun, D. J. Fleet, A. Hertzmann, and P. Fua. Priors for people tracking from small training sets. In ICCV, pages 403-410, 2005.