Non-linear statistical models for the 3D reconstruction of human pose and motion from monocular image sequences

Richard Bowden; T. A. Mitchell; M. Sarhadi

doi:10.1016/s0262-8856(99)00076-1

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Non-linear statistical models for the 3D reconstruction of human pose and motion from monocular image sequences

Richard Bowden T. A. Mitchell M. Sarhadi

Year: 2000 Journal: Image and Vision Computing Vol: 18 (9)Pages: 729-737 Publisher: Elsevier BV

DOI: 10.1016/s0262-8856(99)00076-1

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Keywords:

Computer vision Artificial intelligence Silhouette Computer science Monocular Landmark Position (finance) Monocular vision Articulated body pose estimation Motion (physics) Point (geometry) Point distribution model Tracking (education) Human head Image (mathematics) Mathematics 3D pose estimation Finite element method Geometry

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Human Pose and Action Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Advanced Vision and Imaging

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Video Surveillance and Tracking Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Non-linear statistical models for the 3D reconstruction of human pose and motion from monocular image sequences

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