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Detection and Tracking of People from Laser Range Data
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS). (Intelligent systems' laboratory)
2010 (English)Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Abstract [en]

In this thesis report, some of the most promising techniques, in the field of intelligent vehicles and mobile robotics, for detection and tracking of moving objects in an indoor environment are investigated. Kalman filter (KF), extended Kalman filter (EKF), and particle filters (PF) based techniques for the tracking of people are implemented and evaluated. A heuristic method is then proposed to improve the performance of the EKF based tracking in situations where moving objects are hidden by obstacles. The proposed method is based on points of maximum uncertainty (PMU) in occlusion situations and its complexity and accuracy is compared with PF method. The EKF, PF and PMU based methods are examined and compared using experimental data which are extracted by a laser range finder in an indoor environment with predefined hinders and people as the moving objects.

Place, publisher, year, edition, pages
2010. , p. 89
Keyword [en]
Segmentation, Feature extraction, Movement detection, Tracking, Kalman filter, Extended
Identifiers
URN: urn:nbn:se:hh:diva-6102OAI: oai:DiVA.org:hh-6102DiVA, id: diva2:356237
Uppsok
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Available from: 2010-10-26 Created: 2010-10-11 Last updated: 2010-10-26Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf