Online multi-target tracking using recurrent neural networks

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dc.contributor.authorMilan, A.
dc.contributor.authorRezatofighi, H.
dc.contributor.authorDick, A.
dc.contributor.authorReid, I.
dc.contributor.authorSchindler, K.
dc.contributor.conference31st AAAI Conference on Artificial Intelligence (AAAI 2017) (4 Feb 2017 - 9 Feb 2017 : San Francisco)
dc.date.issued2017
dc.description.abstractWe present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs). Tracking multiple objects in real-world scenes involves many challenges, including a) an a-priori unknown and time-varying number of targets, b) a continuous state estimation of all present targets, and c) a discrete combinatorial problem of data association. Most previous methods involve complex models that require tedious tuning of parameters. Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking. Existing deep learning methods are not designed for the above challenges and cannot be trivially applied to the task. Our solution addresses all of the above points in a principled way. Experiments on both synthetic and real data show promising results obtained at ~300 Hz on a standard CPU, and pave the way towards future research in this direction.
dc.description.statementofresponsibilityAnton Milan, S. Hamid Rezatofighi, Anthony Dick, Ian Reid, Konrad Schindler
dc.identifier.citationProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2017, pp.4225-4232
dc.identifier.issn2159-5399
dc.identifier.issn2374-3468
dc.identifier.orcidDick, A. [0000-0001-9049-7345]
dc.identifier.orcidReid, I. [0000-0001-7790-6423]
dc.identifier.urihttp://hdl.handle.net/2440/116855
dc.language.isoen
dc.publisherASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE
dc.relation.granthttp://purl.org/au-research/grants/arc/LP130100154
dc.relation.granthttp://purl.org/au-research/grants/arc/FL130100102
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100016
dc.relation.ispartofseriesAAAI Conference on Artificial Intelligence
dc.rightsCopyright © 2017, Association for the Advancement of Artificial Intelligence
dc.source.urihttps://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14184
dc.titleOnline multi-target tracking using recurrent neural networks
dc.typeConference paper
pubs.publication-statusPublished

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