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