Human tracking and segmentation supported by silhouette-based gait recognition

  • Wang J
  • Makihara Y
  • Yagi Y
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Abstract

Gait recognition has recently gained attention as an effective approach
to identify individuals at a distance from a camera. Most existing
gait recognition algorithms assume that people have been tracked
and silhouettes have been segmented successfully. Tacking and segmentation
are, however, very difficult especially for articulated objects such
as human beings. Therefore, we present an integrated algorithm for
tracking and segmentation supported by gait recognition. After the
tracking module produces initial results consisting of bounding boxes
and foreground likelihood images, the gait recognition module searches
for the optimal silhouette-based gait models corresponding to the
results. Then, the segmentation module tries to segment people out
using the provided gait silhouette sequence as shape priors. Experiments
on real video sequences show the effectiveness of the proposed approach.

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