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https://hdl.handle.net/2440/116855
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DC Field | Value | Language |
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dc.contributor.author | Milan, A. | - |
dc.contributor.author | Rezatofighi, H. | - |
dc.contributor.author | Dick, A. | - |
dc.contributor.author | Reid, I. | - |
dc.contributor.author | Schindler, K. | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2017, pp.4225-4232 | - |
dc.identifier.issn | 2159-5399 | - |
dc.identifier.issn | 2374-3468 | - |
dc.identifier.uri | http://hdl.handle.net/2440/116855 | - |
dc.description.abstract | We 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.statementofresponsibility | Anton Milan, S. Hamid Rezatofighi, Anthony Dick, Ian Reid, Konrad Schindler | - |
dc.language.iso | en | - |
dc.publisher | ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE | - |
dc.relation.ispartofseries | AAAI Conference on Artificial Intelligence | - |
dc.rights | Copyright © 2017, Association for the Advancement of Artificial Intelligence | - |
dc.source.uri | https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14184 | - |
dc.title | Online multi-target tracking using recurrent neural networks | - |
dc.type | Conference paper | - |
dc.contributor.conference | 31st AAAI Conference on Artificial Intelligence (AAAI 2017) (4 Feb 2017 - 9 Feb 2017 : San Francisco) | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/LP130100154 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/FL130100102 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/CE140100016 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Dick, A. [0000-0001-9049-7345] | - |
dc.identifier.orcid | Reid, I. [0000-0001-7790-6423] | - |
Appears in Collections: | Aurora harvest 3 Australian Institute for Machine Learning publications Computer Science publications |
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