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Tracking People Interacting with Objects
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: 0000-0002-5750-9655
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
2010 (English)In: 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2010, 747-754 p.Conference paper, Published paper (Refereed)
Abstract [en]

While the problem of tracking 3D human motion has been widely studied, most approaches have assumed that the person is isolated and not interacting with the environment. Environmental constraints, however, can greatly constrain and simplify the tracking problem. The most studied constraints involve gravity and contact with the ground plane. We go further to consider interaction with objects in the environment. In many cases, tracking rigid environmental objects is simpler than tracking high-dimensional human motion. When a human is in contact with objects in the world, their poses constrain the pose of body, essentially removing degrees of freedom. Thus what would appear to be a harder problem, combining object and human tracking, is actually simpler. We use a standard formulation of the body tracking problem but add an explicit model of contact with objects. We find that constraints from the world make it possible to track complex articulated human motion in 3D from a monocular camera.

Place, publisher, year, edition, pages
2010. 747-754 p.
Series
IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919
Keyword [en]
Body tracking, Degrees of freedom, Environmental constraints, Environmental objects, Explicit models, Ground planes, High-dimensional, Human motions, Human Tracking, Monocular cameras, Tracking problem, Computer vision, Three dimensional
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-32026DOI: 10.1109/CVPR.2010.5540140ISI: 000287417500096Scopus ID: 2-s2.0-77956006419ISBN: 978-1-4244-6984-0 (print)OAI: oai:DiVA.org:kth-32026DiVA: diva2:408907
Conference
23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, JUN 13-18, 2010
Note
QC 20110406Available from: 2011-04-06 Created: 2011-04-04 Last updated: 2012-01-23Bibliographically approved

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Kjellström, HedvigKragic, Danica

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