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Multi-Target Tracking -- Linking Identities using Bayesian Network Inference
Institute of Computer Science - FORTH.ORCID iD: 0000-0002-7725-0548
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
2006 (English)In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2006, 2187-2194 p.Conference paper, Published paper (Refereed)
Abstract [en]

Multi-target tracking requires locating the targets and labeling their identities. The latter is a challenge when many targets, with indistinct appearances, frequently occlude one another, as in football and surveillance tracking. We present an approach to solving this labeling problem.

When isolated, a target can be tracked and its identity maintained. While, if targets interact this is not always the case. This paper assumes a track graph exists, denoting when targets are isolated and describing how they interact. Measures of similarity between isolated tracks are defined. The goal is to associate the identities of the isolated tracks, by exploiting the graph constraints and similarity measures.

We formulate this as a Bayesian network inference problem, allowing us to use standard message propagation to find the most probable set of paths in an efficient way. The high complexity inevitable in large problems is gracefully reduced by removing dependency links between tracks. We apply the method to a 10 min sequence of an international football game and compare results to ground truth.

Place, publisher, year, edition, pages
Los Alamitos, CA, USA: IEEE Computer Society, 2006. 2187-2194 p.
Keyword [en]
computer vision, multi-target tracking
National Category
Engineering and Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-51095DOI: 10.1109/CVPR.2006.198Scopus ID: 2-s2.0-33845575115ISBN: 0-7695-2597-0 (print)OAI: oai:DiVA.org:kth-51095DiVA: diva2:463428
Conference
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006. New York, NY. 17 June 2006 - 22 June 2006
Note
QC 20111212Available from: 2011-12-09 Created: 2011-12-09 Last updated: 2012-01-19Bibliographically approved

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Nillius, Peter

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CiteExportLink to record
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Citation style
  • apa
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