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ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Multiple View Geometry in Computer Vision
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Statistical modeling for networked video: coding optimization, error concealment and traffic analysis
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A survey of advances in vision-based human motion capture and analysis
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Gibbs sampling approach for generation of truncated multivariate Gaussian random variables
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
Prakash: lighting aware motion capture using photosensing markers and multiplexed illuminators
ACM SIGGRAPH 2007 papers
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ACM SIGGRAPH 2007 papers
Relevant Feature Selection for Human Pose Estimation and Localization in Cluttered Images
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Hierarchical implicit surface joint limits for human body tracking
Computer Vision and Image Understanding
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FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
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IEEE Transactions on Signal Processing
Stochastic kinematic modeling and feature extraction for gait analysis
IEEE Transactions on Image Processing
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This paper presents a low-cost real-time alternative to available commercial human motion capture systems. First, a set of distinguishable markers are placed on several human body landmarks, and the scene is captured by a number of calibrated and synchronized cameras. In order to establish a physical relation among markers, a human body model is defined. Markers are detected on all camera views and delivered as the input of an annealed particle filter scheme where every particle encodes an instance of the pose of the body model to be estimated. Likelihood between particles and input data is performed through the robust generalized symmetric epipolar distance and kinematic constrains are enforced in the propagation step towards avoiding impossible poses. Tests over the HumanEva annotated data set yield quantitative results showing the effectiveness of the proposed algorithm. Results over sequences involving fast and complex motions are also presented.