Tracking interacting targets with laser scanner via on-line supervised learning
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 met...
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Published in | 2008 IEEE International Conference on Robotics and Automation pp. 2271 - 2276 |
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Main Authors | , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.05.2008
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Subjects | |
Online Access | Get full text |
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Summary: | 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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ISBN: | 1424416469 9781424416462 |
ISSN: | 1050-4729 2577-087X |
DOI: | 10.1109/ROBOT.2008.4543552 |