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Computer Science > Artificial Intelligence

arXiv:cs/0510063 (cs)
[Submitted on 21 Oct 2005]

Title:Markerless Human Motion Capture for Gait Analysis

Authors:Jamal Saboune (INRIA Lorraine - LORIA), François Charpillet (INRIA Lorraine - LORIA)
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Abstract: The aim of our study is to detect balance disorders and a tendency towards the falls in the elderly, knowing gait parameters. In this paper we present a new tool for gait analysis based on markerless human motion capture, from camera feeds. The system introduced here, recovers the 3D positions of several key points of the human body while walking. Foreground segmentation, an articulated body model and particle filtering are basic elements of our approach. No dynamic model is used thus this system can be described as generic and simple to implement. A modified particle filtering algorithm, which we call Interval Particle Filtering, is used to reorganise and search through the model's configurations search space in a deterministic optimal way. This algorithm was able to perform human movement tracking with success. Results from the treatment of a single cam feeds are shown and compared to results obtained using a marker based human motion capture system.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:cs/0510063 [cs.AI]
  (or arXiv:cs/0510063v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.cs/0510063

Submission history

From: Jamal Saboune [view email] [via CCSD proxy]
[v1] Fri, 21 Oct 2005 13:45:49 UTC (300 KB)
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