Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
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
Self-Calibration from Image Triplets
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Silhouette Lookup for Automatic Pose Tracking
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Discriminative Density Propagation for 3D Human Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Trees: Common-Factor Models for 2D Human Pose Recovery
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recovering Human Body Configurations Using Pairwise Constraints between Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integration of Local Image Cues for Probabilistic 2D Pose Recovery
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Recovery of upper body poses in static images based on joints detection
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera auto-calibration from articulated motion
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Simultaneous tracking of multiple body parts of interacting persons
Computer Vision and Image Understanding
Multicamera tracking of articulated human motion using shape and motion cues
IEEE Transactions on Image Processing
International Journal of Computer Vision
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Multiple people tracking and pose estimation with occlusion estimation
Computer Vision and Image Understanding
Part template: 3D representation for multiview human pose estimation
Pattern Recognition
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In this paper, we address the recovery of human 2D postures from monocular image sequences. We propose a novel pose estimation framework which is based on the integration of probabilistic bottom-up and top-down processes which iteratively refine each other: foreground pixels are segmented using image cues whereas a hierarchical 2D body model fitting constraints body partitions. Its main advantages are twofold. First, the presented framework is activity-independent since it does not rely on learning any motion model. Secondly, we propose a confidence score indicating the quality of each estimated pose. Our study also reveals significant discrepancy between ground truth joint positions according to whether they are defined by humans or a motion capture system. Quantitative and qualitative results are presented on a variety of video sequences to validate our approach.