A bayesian framework for human body pose tracking from depth image sequences

Sensors

Volumn 10, Issue 5, 2010, Pages 5280-5293

  Zhu, Youding ^a   Fujimura, Kikuo ^a  

^a HONDA RESEARCH INSTITUTE   (United States)

Author keywords

Bayesian inference; Depth images; Human pose tracking

Indexed keywords

BAYESIAN INFERENCE; DENSE CORRESPONDENCES; DEPTH IMAGE; HUMAN POSE TRACKING; IMAGE-BASED LOCALIZATIONS; LOCAL OPTIMIZATION METHODS; NUMBER OF DEGREES OF FREEDOM; PERFORMANCE COMPARISON;

BAYESIAN NETWORKS; COMPUTER VISION; HUMAN FORM MODELS; INFERENCE ENGINES; RECOVERY;

GESTURE RECOGNITION;

ALGORITHM; ARTICLE; AUDIOVISUAL EQUIPMENT; BAYES THEOREM; BAYESIAN INFERENCE; BIOMECHANICS; BODY POSTURE; DEPTH IMAGES; HUMAN; HUMAN POSE TRACKING; METHODOLOGY; THREE DIMENSIONAL IMAGING;

BAYESIAN INFERENCE; DEPTH IMAGES; HUMAN POSE TRACKING;

ALGORITHMS; BAYES THEOREM; BIOMECHANICAL PHENOMENA; HUMANS; IMAGING, THREE-DIMENSIONAL; MODELS, ANATOMIC; POSTURE;

EID: 77953496851     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s100505280     Document Type: Article

References (13)

1. D.; Woetzel, J.; Koch, R. Nonlinear body pose estimation from depth images. In Proceedings of the 27th Annual Meeting of the German Association for Pattern Recognition, Vienna, Austria, August 30-September 2, 2005.
2. S.; Vacek, S.; Dillmann, R. Sensor fusion for 3d human body tracking with an articulated 3d body model. In Proceedings of the International Conference on Robotics and Automation, Orlando, FL, USA, May 15-19, 2006; pp. 1686-1691.
3. Ziegler, J.; Nickel, K.; Stiefelhagen, R. Tracking of the articulated upper body on multi-view stereo image sequences. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, USA, June 17-22, 2006; pp. 774-781.
4. Y.; Fujimura, K. Constrained optimization for human pose estimation from depth sequences. In Proceedings of IEEE Asian Conference on Computer Vision, Tokyo, Japan, November 2007.
5. Sminchisescu, C.; Triggs, B. Kinematic jump processes for monocular 3d human tracking. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Madison, WI, USA, June 16-22, 2003; pp. 18-20.
6. Lee, M.W.; Cohen, I. Proposal maps driven mcmc for estimating human body pose in static images. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,Washington, DC, USA, June 27-July 2, 2004; pp. 334-341.
7. Besl, P.; McKay, N. A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 1992, 14, 239-256.
8. Lewis, J.P.; Cordner, M.; Fong, N. Pose space deformations: A unified approach to shape interpolation and skeleton-driven deformation. In Proceedings of SIGGRAPH 2000, New Orleans, LA, USA, July 23-28, 2000; pp. 165-172.
9. Zhu, Y.; Dariush, B.; Fujimura, K. Controlled human pose estimation from depth image streams. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Time-of-Flight Workshop (CVPRW'08), Anchorage, AK, USA, June 23-28, 2008.
10. Cham, T.J.; Rehg, J. A multiple hypothesis approach to figure tracking. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'99), Ft. Collins, CO, USA, June 23-25, 1999; pp. 239-245.
11. Demirdjian, D.; Taycher, L.; Shakhnarovich, G.; Grauman, K.; Darrell, T. Avoiding the streetlight effect: Tracking by exploring likelihood modes. In Proceedings of IEEE International Conference on Computer Vision (ICCV'05), Beijing, China, October 17-20, 2005; pp. 357-364.
12. Morris, D.; Rehg, J. Singularity analysis for articulated object tracking. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'98), Santa Barbara, CA, USA, June 23-25, 1998; pp. 189-196.
13. SwissRanger. Online Time-of-Flight Camera Information. Available online: http://www.mesaimaging.ch/prodviews.php (accessed on 5 March 2010).