Authors: Xuan Song, Jinshi Cui, Xulei Wang, Huijing Zhao and Hongbin Zha
From: ICRA 2008
Abstract: Successful multi-target tracking requires
locating the targets and labeling their identities. For the laser
based tracking system, the latter becomes significantly more
challenging when the targets frequently interact with each
other. This paper presents a novel on-line supervised learning
based method for tracking interacting targets with laser
scanner. When the targets do not interact with each other, we
collect samples and train a classifier for each target. When the
targets are in close proximity, we use these classifiers to assist
in tracking. Different evaluations demonstrate that this
method has a better tracking performance than previous
methods when interactions occur, and can maintain correct
tracking under various complex tracking situations.
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