Multi-Target Tracking - Linking Identities using Bayesian Network Inference

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2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)

New York, NY

June 17-June 22

ISBN: 0-7695-2597-0

	ASCII Text	x
	Peter Nillius, Josephine Sullivan, Stefan Carlsson, "Multi-Target Tracking - Linking Identities using Bayesian Network Inference," Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 2, pp. 2187-2194, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06), 2006.

	BibTex	x
	@article{ 10.1109/CVPR.2006.198, author = {Peter Nillius and Josephine Sullivan and Stefan Carlsson}, title = {Multi-Target Tracking - Linking Identities using Bayesian Network Inference}, journal ={Computer Vision and Pattern Recognition, IEEE Computer Society Conference on}, volume = {2}, year = {2006}, issn = {1063-6919}, pages = {2187-2194}, doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2006.198}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on TI - Multi-Target Tracking - Linking Identities using Bayesian Network Inference SN - 1063-6919 SP2187 EP2194 A1 - Peter Nillius, A1 - Josephine Sullivan, A1 - Stefan Carlsson, PY - 2006 KW - null VL - 2 JA - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on ER -

Peter Nillius, Institute of Computer Science - FORTH, Greece

Josephine Sullivan, Royal Institute of Technology (KTH), Sweden

Stefan Carlsson, Royal Institute of Technology (KTH), Sweden

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2006.198

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.

Citation:

Peter Nillius, Josephine Sullivan, Stefan Carlsson, "Multi-Target Tracking - Linking Identities using Bayesian Network Inference," cvpr, vol. 2, pp.2187-2194, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06), 2006

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