An intelligent fitting room using multi-camera perception
Proceedings of the 13th international conference on Intelligent user interfaces
Recovery of upper body poses in static images based on joints detection
Pattern Recognition Letters
Action-specific motion prior for efficient Bayesian 3D human body tracking
Pattern Recognition
Real-time robust body part tracking for augmented reality interface
Proceedings of the 8th International Conference on Virtual Reality Continuum and its Applications in Industry
Understanding transit scenes: a survey on human behavior-recognition algorithms
IEEE Transactions on Intelligent Transportation Systems
Robust Pose Recognition of the Obscured Human Body
International Journal of Computer Vision
Estimation of human orientation in images captured with a range camera
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Arbitrary body segmentation in static images
Pattern Recognition
Combination of annealing particle filter and belief propagation for 3D upper body tracking
Applied Bionics and Biomechanics - Personal Care Robotics
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Accurate 3-D human body pose tracking from a monocular video stream is important for a number of applications. We describe a novel hierarchical approach for tracking human pose that uses edge-based features during the coarse stage and later other features for global optimization. At first, humans are detected by motion and tracked by fitting an ellipse in the image. Then, body components are found using edge features and used to estimate the 2D positions of the body joints accurately. This helps to bootstrap the estimation of 3D pose using a sampling-based search method in the last stage. We present experiment results with sequences of different realistic scenes to illustrate the performance of the method.