Silhouette representation and matching for 3D pose discrimination - A comparative study
Image and Vision Computing
Self-occlusion handling for human body motion tracking from 3D ToF image sequence
Proceedings of the 1st international workshop on 3D video processing
Real time multiple people tracking and pose estimation
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
3D human pose recovery from image by efficient visual feature selection
Computer Vision and Image Understanding
Multiview human pose estimation with unconstrained motions
Pattern Recognition Letters
Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis
Computer Vision and Image Understanding
Multi-view 3D Human Pose Estimation in Complex Environment
International Journal of Computer Vision
Multiple people tracking and pose estimation with occlusion estimation
Computer Vision and Image Understanding
Hierarchical pose estimation for human gait analysis
Computer Methods and Programs in Biomedicine
Graph based semi-supervised human pose estimation: When the output space comes to help
Pattern Recognition Letters
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Human action recognition employing negative space features
Journal of Visual Communication and Image Representation
Tracking in object action space
Computer Vision and Image Understanding
Vector field analysis for multi-object behavior modeling
Image and Vision Computing
Discriminative fusion of shape and appearance features for human pose estimation
Pattern Recognition
Modeling multi-object interactions using "string of feature graphs"
Computer Vision and Image Understanding
Fast action recognition using negative space features
Expert Systems with Applications: An International Journal
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Tracking human body poses in monocular video has many important applications. The problem is challenging in realistic scenes due to background clutter, variation in human appearance and self-occlusion. The complexity of pose tracking is further increased when there are multiple people whose bodies may inter-occlude. We proposed a three-stage approach with multi-level state representation that enables a hierarchical estimation of 3D body poses. Our method addresses various issues including automatic initialization, data association, self and inter-occlusion. At the first stage, humans are tracked as foreground blobs and their positions and sizes are coarsely estimated. In the second stage, parts such as face, shoulders and limbs are detected using various cues and the results are combined by a grid-based belief propagation algorithm to infer 2D joint positions. The derived belief maps are used as proposal functions in the third stage to infer the 3D pose using data-driven Markov chain Monte Carlo. Experimental results on several realistic indoor video sequences show that the method is able to track multiple persons during complex movement including sitting and turning movements with self and inter-occlusion.