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SIGMAP 2007 - Proceedings of the Second International Conference on Signal Processing and Multimedia Applications, Barcelona, Spain, July 28-31, 2007, SIGMAP is part of ICETE - The International Joint Conference on e-Business and Telecommunications; 01/2007
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Advanced Video and Signal Based Surveillance, 2005 IEEE International Conference on Video and Signal Based Surveillance (AVSS'05), 15-16 September 2005, Como, Italy.; 01/2005
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ABSTRACT: We propose a human body action classifier based on a 3D representation of the body in terms of volumetric coordi-nates. Features representing body postures are extracted di-rectly from 3D data, making the system inherently insensi-tive to viewpoint dependence, motion ambiguities and self-occlusions. An Invariant Shape Descriptor of human body is obtained in order to capture only posture-dependent char-acteristics, despite possible differences in translation, orien-tation, scale and body size. Frame-by-frame descriptions, generated from a gesture sequence, are collected together in matrices. Clustering of action matrices is eventually per-formed, and through DTW (Dynamic Time Warping) (while computing the distance metric), we gain independence from possible temporal nonlinear distortions among different in-stances of the same gesture.
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ABSTRACT: Markerless human tracking addresses the problem of estimating human body motion in non-cooperative environments. Computer Vision techniques combined with Pattern Recognition theory serve the purpose of extracting information on human body postures from video-sequences, without the need of wearable markers. Multi-camera systems further enhance this kind of application providing frames from multiple viewpoints. This work tackles the application of multi-camera posture estimation through the use of a multi-camera environment, also known as "smart space". A 3D skeleton structure and geometrical descriptors of human muscles are fitted to the volumetric data to directly recover 3D information. 3D skeleton deformations and bio-mechanical constraints on joint models are used to provide posture information at each frame. The proposed system does not require any pre-initialization phase and automatically adapt the skeleton and the volumetric occupation of each limb to the actor physiognomy independently from the pose. Exhaustive tests were performed to validate our approach.
