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Multi-sensor multi-person tracking on a mobile robot platform

  • Service robots need to be aware of persons in their vicinity in order to interact with them. People tracking enables the robot to perceive persons by fusing the information of several sensors. Most robots rely on laser range scanners and RGB cameras for this task. The thesis focuses on the detection and tracking of heads. This allows the robot to establish eye contact, which makes interactions feel more natural. Developing a fast and reliable pose invariant head detector is challenging. The head detector that is proposed in this thesis works well on frontal heads, but is not fully pose-invariant. This thesis further explores adaptive tracking to keep track of heads that do not face the robot. Finally, head detector and adaptive tracker are combined within a new people tracking framework and experiments show its effectiveness compared to a state-of the-art system.

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Metadaten
URL:http://tubaf.qucosa.de/landing-page/?tx_dlf[id]=http%3A%2F%2Ftubaf.qucosa.de%2Fapi%2Fqucosa%253A23219%2Fmets
Place of publication:Freiberg
Contributor(s):Matthias Rätsch
Referee:Bernhard Jung, Hans-Joachim Böhme, Horst-Michael Groß
Referee of HS Reutlingen:Rätsch, Matthias
Document Type:Doctoral Thesis
Language:English
Year of Publication:2018
Date of final exam:2018/02/01
Tag:Gesichtserkennung; Objektverfolgung; Serviceroboter
Pagenumber:113
First Page:1
Last Page:113
City of event:Freiberg
Dissertation note:Dissertation, Technische Universität Bergakademie Freiberg, 2018
Dewey Decimal Classification:004 Informatik
Open Access:Ja
Licence (German):License Logo  Open Access