Abstract:
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End-effectors are considered to be the main topological extremities
of a given 3D body. Even if the nature of such body is not restricted,
this paper focuses on the human body case. Detection of human
extremities is a key issue in the human motion capture domain, being
needed to initialize and update the tracker. Therefore, the effectiveness
of human motion capture systems usually depends on the
reliability of the obtained end-effectors. The increasing accuracy,
low cost and easy installation of depth cameras has opened the door
to new strategies to overcome the body pose estimation problem.
With the objective of detecting the head, hands and feet of a human
body, we propose a new local feature computed from depth data,
which gives an idea of its curvature and prominence. Such feature is
weighted depending on recent detections, providing also a temporal
dimension. Based on this feature, some end-effector candidate blobs
are obtained and classified into head, hands and feet according to
three probabilistic descriptors. |