Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Learning to Parse Pictures of People
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
On the Relationship of Human Walking and Running: Automatic Person Identification by Gait
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Tracking of Persons in Monocular Image Sequences
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gender Recognition from Walking Movements using Adaptive Three-Mode PCA
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Human Pose Tracking in Monocular Sequence Using Multilevel Structured Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
Editorial: On designing new environments for ageing
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
Articulated structures like the human body have many degrees of freedom. This makes an evaluation of the configuration's likelihood very challenging. In this work we propose new linked hierarchical graphical models which are able to efficiently evaluate likelihoods of articulated structures by sharing visual primitives. Instead of evaluating all configurations of the human body separately we take advantage of the fact that different configurations of the human body share body parts, and body parts, in turn, share visual primitives. A hierarchical Markov random field is used to integrate the sharing of visual primitives in a probabilistic framework. We propose a scalable hierarchical representation of the human body and show that this representation is especially well suited for human gait analysis from a frontal camera perspective. Furthermore, the results of the evaluation on a gait dataset show that sharing primitives substantially accelerates the evaluation and that our hierarchical probabilistic framework is a robust method for scalable detection of the human body.