Towards model-based recognition of human movements in image sequences
CVGIP: Image Understanding
Pictorial Structures for Object Recognition
International Journal of Computer Vision
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
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Constraint Integration for Efficient Multiview Pose Estimation with Self-Occlusions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast nonparametric belief propagation for real-time stereo articulated body tracking
Computer Vision and Image Understanding
Human Pose Tracking in Monocular Sequence Using Multilevel Structured Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Sign Language Spotting with a Threshold Model Based on Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Human Motion Tracking with a Coordinated Mixture of Factor Analyzers
International Journal of Computer Vision
International Journal of Computer Vision
A Study on Smoothing for Particle-Filtered 3D Human Body Tracking
International Journal of Computer Vision
Variable silhouette energy image representations for recognizing human actions
Image and Vision Computing
Dual gait generative models for human motion estimation from a single camera
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Proposal maps driven MCMC for estimating human body pose in static images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Variational mixture smoothing for non-linear dynamical systems
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Tracking with Occlusions via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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Tracking human poses in video can be considered as the process of inferring the positions of the body joints. Among various obstacles to this task, one of the most challenging is to deal with 'self-occlusion', where one body part occludes another one. In order to tackle this problem, a model must represent the self-occlusion between different body parts which leads to complex inference problems. In this paper, we propose a method that estimates occlusion states adaptively. A Markov random field is used to represent the occlusion relationship between human body parts in terms an occlusion state variable, which represents the depth order. To ensure efficient computation, inference is divided into two steps: a body pose inference step and an occlusion state inference step. We test our method using video sequences from the HumanEva dataset. We label the data to quantify how the relative depth ordering of parts, and hence the self-occlusion, changes during the video sequence. Then we demonstrate that our method can successfully track human poses even when there are frequent occlusion changes. We compare our approach to alternative methods including the state of the art approach which use multiple cameras.