From image sequences towards conceptual descriptions
Image and Vision Computing
Active shape models—their training and application
Computer Vision and Image Understanding
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking persons in monocular image sequences
Computer Vision and Image Understanding
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
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Dynamic Models of Human Motion
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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
Motion Primitives and Probabilistic Edit Distance for Action Recognition
Gesture-Based Human-Computer Interaction and Simulation
View-invariant gesture recognition using 3D optical flow and harmonic motion context
Computer Vision and Image Understanding
Action recognition using motion primitives and probabilistic edit distance
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
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Human behavior analysis is an open problem in the computer vision community. The aim of this paper is to model human actions. We present a taxonomy in order to discuss about a knowledge-based classification of human behavior. A novel human action model is presented, called the aSpace, based on a Point Distribution Model (PDM). This representation is compact, accurate and specific. The human body model is represented as a stick figure, and several sequences of humans actions are used to compute the aSpace. In order to test our action representation, two applications are provided: recognition and synthesis of actions.