Towards model-based recognition of human movements in image sequences
CVGIP: Image Understanding
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Reconstruction of articulated objects from point correspondences in a single uncalibrated image
Computer Vision and Image Understanding
Estimating anthropometry and pose from a single uncalibrated image
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Estimating Human Body Configurations Using Shape Context Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Recognizing and Tracking Human Action
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Order Structure, Correspondence, and Shape Based Categories
Shape, Contour and Grouping in Computer Vision
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Model-based tracking of self-occluding articulated objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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This paper describes a system aimed at automising the reconstruction of human motion. Human motion can be described as a sequence of 3D body postures. View based recognition of these postures forms the basis of human tracking algorithms [18]. These postures are defined by the underlying skeleton, an articulated structure of rigid links connected at rotational joints. The skeleton can be reconstructed if the rotational joints are tracked [11]. A set of posture specific key frames with pre defined joint locations are stored. Joint locations from these key frames can be mapped to actual frames once correspondence between the two shapes has been achieved. The rotational joints are in general not well defined in 2D images thus the iterative process of successively repeating point localisation and 3D reconstruction allows one to impose the geometric definition on the points. The power of the approach presented is demonstrated by the recognition, self calibration and 3D reconstruction of a tennis stroke seen from two cameras achieved without precalibrated cameras or manual intervention for initialisation and error recovery.