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3D Markerless Human Limb Localization through Robust Energy Minimization

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.
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https://hal.inria.fr/inria-00326757
Contributor : Peter Sturm <>
Submitted on : Sunday, October 5, 2008 - 2:02:37 PM
Last modification on : Tuesday, August 13, 2019 - 11:10:03 AM
Long-term archiving on: : Monday, October 8, 2012 - 1:57:45 PM

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  • HAL Id : inria-00326757, version 1

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Marco Marcon, Massimiliano Pierobon, Augusto Sarti, Stefano Tubaro. 3D Markerless Human Limb Localization through Robust Energy Minimization. Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2 2008, Andrea Cavallaro and Hamid Aghajan, Oct 2008, Marseille, France. ⟨inria-00326757⟩

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