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dc.contributor.authorCao, Xiaoyan
dc.contributor.authorGuo, Shihui
dc.contributor.authorLin, Juncong
dc.contributor.authorZhang, Wenshu
dc.contributor.authorLiao, Minghong
dc.date.accessioned2020-10-16T12:58:53Z
dc.date.available2020-10-16T12:58:53Z
dc.date.issued2020-02-21
dc.identifier.citationCao, X., Guo, S., Lin, J., Zhang, W. and Liao, M. (2020) 'Online tracking of ants based on deep association metrics: method, dataset and evaluation', Pattern Recognition, p.107233. https://doi.org/10.1016/j.patcog.2020.107233en_US
dc.identifier.issn0031-3203
dc.identifier.urihttp://hdl.handle.net/10369/11173
dc.descriptionArticle published in Pattern Recognition available at https://doi.org/10.1016/j.patcog.2020.107233en_US
dc.description.abstractTracking movement of insects in a social group (such as ants) is challenging, because the individuals are not only similar in appearance but also likely to perform intensive body contact and sudden movement adjustment (start/stop, direction changes). To address this challenge, we introduce an online multi-object tracking framework that combines both the motion and appearance information of ants. We obtain the appearance descriptors by using the ResNet model for offline training on a small (N=50) sample dataset. For online association, a cosine similarity metric computes the matching degree between historical appearance sequences of the trajectory and the current detection. We validate our method in both indoor (lab setup) and outdoor video sequences. The results show that our model obtains 99.3% ± 0.5% MOTA and 91.9% ± 2.1% MOTP across 24,050 testing samples in five indoor sequences, with real-time tracking performance. In an outdoor sequence, we achieve 99.3% MOTA and 92.9% MOTP across 22,041 testing samples. The datasets and code are made publicly available for future research in relevant domains.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesPattern Recognition;
dc.subjectAnt trackingen_US
dc.subjectResNet modelen_US
dc.subjectMahalanobis distanceen_US
dc.subjectAppearance descriptorsen_US
dc.titleOnline tracking of ants based on deep association metrics: method, dataset and evaluationen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1016/j.patcog.2020.107233
dcterms.dateAccepted2020-01-23
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US
rioxxterms.versionAMen_US
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserveden_US
rioxxterms.licenseref.startdate2022-02-21
dc.refexceptionThere was a delay in securing the final peer-reviewed text
rioxxterms.freetoread.startdate2022-02-21
rioxxterms.funder.project37baf166-7129-4cd4-b6a1-507454d1372een_US


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