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
Model based human motion tracking using probability evolutionary algorithm
Pattern Recognition Letters
Staying Well Grounded in Markerless Motion Capture
Proceedings of the 30th DAGM symposium on Pattern Recognition
Human body pose estimation with particle swarm optimisation
Evolutionary Computation
Action-specific motion prior for efficient Bayesian 3D human body tracking
Pattern Recognition
International Journal of Computer Vision
Marker-less 3D feature tracking for mesh-based human motion capture
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Online visual tracking with histograms and articulating blocks
Computer Vision and Image Understanding
Real time multiple people tracking and pose estimation
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
International Journal of Computer Vision
Motion Sequence-Based Human Abnormality Detection Scheme for Smart Spaces
Wireless Personal Communications: An International Journal
Parallel appearance-adaptive models for real-time object tracking using particle swarm optimization
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Multi-view 3D Human Pose Estimation in Complex Environment
International Journal of Computer Vision
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Hierarchical querying scheme of human motions for smart home environment
Engineering Applications of Artificial Intelligence
Dual-force metric learning for robust distracter-resistant tracker
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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The detection and tracking of three-dimensional human body models has progressed rapidly but successful approaches typically rely on accurate foreground silhouettes obtained using background segmentation. There are many practical applications where such information is imprecise. Here we develop a new image likelihood function based on the visual appearance of the subject being tracked. We propose a robust, adaptive, appearance model based on the Wandering-Stable-Lost framework extended to the case of articulated body parts. The method models appearance using a mixture model that includes an adaptive template, frame-to-frame matching and an outlier process. We employ an annealed particle filtering algorithm for inference and take advantage of the 3D body model to predict selfocclusion and improve pose estimation accuracy. Quantitative tracking results are presented for a walking sequence with a 180 degree turn, captured with four synchronized and calibrated cameras and containing significant appearance changes and self-occlusion in each view.