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Robust Statistics for 3D Object Tracking
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
2006 (English)In: 2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), VOLS 1-10, 2006, p. 2403-2408Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on methods that enhance performance of a model based 3D object tracking system. Three statistical methods and an improved edge detector are discussed and compared. The evaluation is performed on a number of characteristic sequences incorporating shift, rotation, texture, weak illumination and occlusion. Considering the deviations of the pose parameters from ground truth, it is shown that improving the measurements' accuracy in the detection step yields better results than improving contaminated measurements with statistical means.

Place, publisher, year, edition, pages
2006. p. 2403-2408
Series
IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ISSN 1050-4729
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-63791DOI: 10.1109/ROBOT.2006.1642062ISI: 000240886905023Scopus ID: 2-s2.0-33845617225OAI: oai:DiVA.org:kth-63791DiVA, id: diva2:482604
Conference
IEEE International Conference on Robotics and Automation (ICRA). Orlando, FL. MAY 15-19, 2006
Note
QC 20120127Available from: 2012-01-24 Created: 2012-01-24 Last updated: 2018-01-12Bibliographically approved

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Kragic, Danica

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