Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities

We present an approach to laser-based people tracking using a multi-hypothesis tracker that detects and tracks legs separately with Kalman filters, constant velocity motion models, and a multi-hypothesis data association strategy. People are defined as high-level tracks consisting of two legs that a...

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Bibliographic Details
Published in2008 IEEE International Conference on Robotics and Automation pp. 1710 - 1715
Main Authors Arras, K.O., Grzonka, S., Luber, M., Burgard, W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2008
Subjects
Foot
Kalman filters
Laser modes
Leg
Motion detection
Robotics and automation
Robots
Target tracking
Torso
USA Councils
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ISBN1424416469
9781424416462
ISSN1050-4729
DOI10.1109/ROBOT.2008.4543447

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Summary:We present an approach to laser-based people tracking using a multi-hypothesis tracker that detects and tracks legs separately with Kalman filters, constant velocity motion models, and a multi-hypothesis data association strategy. People are defined as high-level tracks consisting of two legs that are found with little model knowledge. We extend the data association so that it explicitly handles track occlusions in addition to detections and deletions. Additionally, we adapt the corresponding probabilities in a situation-dependent fashion so as to reflect the fact that legs frequently occlude each other. Experimental results carried out with a mobile robot illustrate that our approach can robustly and efficiently track multiple people even in situations of high levels of occlusion.
ISBN:1424416469
9781424416462
ISSN:1050-4729
DOI:10.1109/ROBOT.2008.4543447