Estimation of a 3D motion field from a multi-camera array using a multiresolution Gaussian mixture model

Abstract : The problem of modelling geometry for video based rendering has been much studied in recent years, due to the growing interest in `free viewpoint' video and similar applications. Common approaches fall into two categories: those which approximate surfaces from dense depth maps obtained by generalisations of stereopsis and those which employ an explicit geometric representation such as a mesh. While the former have generality with respect to geometry, they are limited in terms of viewpoint; the latter, on the other hand, sacrifice generality of geometry for freedom to pick an arbitary viewpoint. The purpose of the work reported here is to bridge this gap in object representation, by employing a stochastic model of object structure: a multiresolution Gaussian mixture. Estimation of the model and tracking it through time from multiple cameras is achieved by a multiresolution stochastic simulation. After a brief outline of the method, its use in modelling human motion using data from local and other sources is presented to illustrate its effectiveness compared to the current state of the art.
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Communication dans un congrès
Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2 2008, Oct 2008, Marseille, France. 2008
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  • HAL Id : inria-00326787, version 1

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R. Wilson, A. Bowen, A. Mullins, N.M. Rajpoot. Estimation of a 3D motion field from a multi-camera array using a multiresolution Gaussian mixture model. Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2 2008, Oct 2008, Marseille, France. 2008. 〈inria-00326787〉

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