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Using Interval Particle Filtering for Marker less 3D Human Motion Capture

Jamal Saboune 1 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper we present a new approach for marker less human motion capture from conventional camera feeds. The aim of our study is to recover 3D positions of key points of the body that can serve for gait analysis. Our approach is based on foreground segmentation, an articulated body model and particle filters. In order to be generic and simple no restrictive dynamic modelling was used. A new modified particle filtering algorithm was introduced. It is used efficiently to search the model configuration space. This new algorithm which we call Interval Particle Filtering reorganizes the configurations search space in an optimal deterministic way and proved to be efficient in tracking natural human movement. Results for human motion capture from a single camera are presented and compared to results obtained from a marker based system. The system proved to be able to track motion successfully even in partial occlusions.
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https://hal.inria.fr/inria-00000475
Contributor : Jamal Saboune <>
Submitted on : Friday, October 21, 2005 - 3:34:34 PM
Last modification on : Friday, February 26, 2021 - 3:28:04 PM
Long-term archiving on: : Monday, June 27, 2011 - 3:03:04 PM

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Jamal Saboune, François Charpillet. Using Interval Particle Filtering for Marker less 3D Human Motion Capture. 17th IEEE International Conference on Tools with Artificial Intelligence - ICTAI'05, Nov 2005, Hong Kong, China. pp.621-627. ⟨inria-00000475v2⟩

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