A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Action Spaces for Efficient Bayesian Tracking of Human Motion
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Faster Algorithms for Optimal Multiple Sequence Alignment Based on Pairwise Comparisons
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
3D action modeling and reconstruction for 2d human body tracking
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Posture constraints for bayesian human motion tracking
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Approximate Dynamic Programming for Communication-Constrained Sensor Network Management
IEEE Transactions on Signal Processing
Real-Time Stereo Matching Using Orthogonal Reliability-Based Dynamic Programming
IEEE Transactions on Image Processing
Multi-object detection and tracking by stereo vision
Pattern Recognition
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A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes.