Human skeleton tracking from depth data using geodesic distances and optical flow

Image and Vision Computing

Volumn 30, Issue 3, 2012, Pages 217-226

  Schwarz, Loren Arthur ^a   Mkhitaryan, Artashes ^a   Mateus, Diana ^a   Navab, Nassir ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Depth imaging; Geodesic distances; Human pose estimation

Indexed keywords

GEODESY; INVERSE KINEMATICS; MUSCULOSKELETAL SYSTEM; OPTICAL FLOWS;

3D DATA; ANATOMICAL LANDMARKS; APPEARANCE BASED; BODY MODELS; BODY MOVEMENTS; BODY PARTS; DEPTH IMAGING; FULL BODY; GEODESIC DISTANCES; GRAPH-BASED REPRESENTATIONS; HUMAN POSE ESTIMATIONS; HUMAN SKELETON; INTENSITY IMAGES; INTEREST POINTS; MOTION INFORMATION; POSE ESTIMATION; POSE TRACKING; QUANTITATIVE EVALUATION; TIME OF FLIGHT;

THREE DIMENSIONAL;

EID: 84859817898     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2011.12.001     Document Type: Conference Paper

References (29)

1. R. Johnson, K. O'Hara, A. Sellen, C. Cousins, and A. Criminisi Exploring the potential for touchless interaction in image-guided interventional radiology ACM Conference on Computer-Human Interaction (CHI) 2011
2. R. Urtasun, and T. Darrell Sparse probabilistic regression for activity-independent human pose inference IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2008
3. T. Jaeggli, E. Koller-Meier, and L.V. Gool Learning generative models for multi-activity body pose estimation Int. J. Comput. Vis. 83 2 2009 121 134
4. R. Kehl, and L. Gool Markerless tracking of complex human motions from multiple views Comput. Vis. Image Underst. 104 2 2006 190 209
5. J. Bandouch, F. Engstler, and M. Beetz Accurate human motion capture using an ergonomics-based anthropometric human model Articulated Motion and Deformable Objects (AMDO) 2008
6. R. Hu, A. Hartfiel, J. Tung, A. Fakih, J. Hoey, and P. Poupart 3D pose tracking of walker users' lower limb with a structured-light camera on a moving platform Computer Vision and Pattern Recognition Workshops 2011
7. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake Real-time human pose recognition in parts from single depth images IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011
8. A. Kolb, E. Barth, R. Koch, and R. Larsen Time-of-flight sensors in computer graphics EUROGRAPHICS 2009 119 134
9. V. Ganapathi, C. Plagemann, D. Koller, and S. Thrun Real time motion capture using a single time-of-flight camera IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2010
10. L.A. Schwarz, D. Mateus, V. Castaneda, and N. Navab Manifold learning for tof-based human body tracking and activity recognition British Machine Vision Conference (BMVC) 2010
11. C. Plagemann, V. Ganapathi, and D. Koller Real-time identification and localization of body parts from depth images IEEE International Conference on Robotics and Automation (ICRA) 2010
12. M. Mortara, G. Patane, and M. Spagnuolo From geometric to semantic human body models Comput. Graph. 30 2006 185 196
13. Y. Sun, M. Bray, A. Thayananthan, B. Yuan, and P. Torr Regression-based human motion capture from voxel data British Machine Vision Conference (BMVC) 2006
14. A. Fossati, M. Salzmann, and P. Fua Observable subspaces for 3D human motion recovery IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2009
15. Y. Zhu, B. Dariush, and K. Fujimura Controlled human pose estimation from depth image streams Computer Vision and Pattern Recognition Workshops 2008
16. G. Pons-Moll, A. Baak, T. Helten, M. Müller, H.-P. Seidel, and B. Rosenhahn Multisensor-fusion for 3D full-body human motion capture IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2010
17. V. John, E. Trucco, and S. Ivekovic Markerless human articulated tracking using hierarchical particle swarm optimisation Image Vis. Comput. 28 11 2010 1530 1547
18. H. Ning, T. Tan, L. Wang, and W. Hu Kinematics-based tracking of human walking in monocular video sequences Image Vis. Comput. 22 5 2004 429 441
19. A. Bleiweiss, and E. Kutliroff Markerless motion capture using a single depth sensor ACM SIGGRAPH ASIA Sketches 2009
20. S. Soutschek, J. Penne, J. Hornegger, and J. Kornhuber 3-D gesture-based scene navigation in medical imaging applications using time-of-flight cameras Computer Vision and Pattern Recognition Workshops 2008
21. R. Jensen, R. Paulsen, and R. Larsen Analyzing gait using a time-of-flight camera Scandinavian Conference on Image Analysis 2009 21 30
22. Y. Zhu, and K. Fujimura A bayesian framework for human body pose tracking from depth image sequences Sensors 10 5 2010 5280 5293
23. S. Malassiotis, and M. Strintzis Real-time hand posture recognition using range data Image Vis. Comput. 26 7 2008 1027 1037
24. M.B. Holte, T.B. Moeslund, and P. Fihl Fusion of range and intensity information for view invariant gesture recognition Computer Vision and Pattern Recognition Workshops 2008
25. S. Denman, V. Chandran, and S. Sridharan An adaptive optical flow technique for person tracking systems Pattern Recognit. Lett. 28 10 2007 1232 1239
26. R. Okada, Y. Shirai, and J. Miura Tracking a person with 3-D motion by integrating optical flow and depth IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2000
27. L.A. Schwarz, A. Mkhitaryan, D. Mateus, and N. Navab Estimating human 3D pose from time-of-flight images based on geodesic distances and optical flow IEEE Conference on Automatic Face and Gesture Recognition (FG) 2011
28. PrimeSense, Inc., NITE Middleware http://www.primesense.com/Nite/ 2010
29. J.F. Obrien, B. Bodenheimer, G. Brostow, and J. Hodgins Automatic joint parameter estimation from magnetic motion capture data Graph. Interface 2000 pp. 53-60