BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Cardboard People: A Parameterized Model of Articulated Image Motion
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Ghost: A Human Body Part Labeling System Using Silhouettes
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
3D Articulated Models and Multi-View Tracking with Silhouettes
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A MAchine Learning Approach for Human Posture Detection in Domotics Application
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Inference of Human Postures by Classification of 3D Human Body Shape
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design and assessment of an intelligent activity monitoring platform
EURASIP Journal on Applied Signal Processing
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This paper proposes an approach to recognise human postures in video sequences, which combines a 2D approach with a 3D human model. The 3D model is a realistic articulated human model which is used to obtain reference postures to compare with test postures. Several 2D approaches using different silhouette representations are compared with each other: projections of moving pixels on the reference axis, Hu moments and skeletonisation. We are interested in a set of specific postures which are representative of typical video understanding applications. We describe results for recognition of general postures (e.g. standing) and detailed postures (e.g. standing with one arm up) in ambiguous/optimal viewpoint with good/bad segmented silhouette to show the effectiveness of our approach.