Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111925
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dc.contributor.authorWilliams, J.en
dc.contributor.authorCarneiro, G.en
dc.contributor.authorSuter, D.en
dc.date.issued2017en
dc.identifier.citationProceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017), 2017 / Guo, Y., Li, H., Cai, W., Murshed, M., Wang, Z., Gao, J., Feng, D. (ed./s), vol.2017-December, pp.580-587en
dc.identifier.isbn1538628406en
dc.identifier.isbn9781538628409en
dc.identifier.urihttp://hdl.handle.net/2440/111925-
dc.description.abstractWe present the Region of Interest Autoencoder (ROIAE), a combined supervised and reconstruction model for the automatic visual detection of objects. More specifically, we augment the detection loss function with a reconstruction loss that targets only foreground examples. This allows us to exploit more effectively the information available in the sparsely populated foreground training data used in common detection problems. Using this training strategy we improve the accuracy of deep learning detection models. We carry out experiments on the Caltech-USA pedestrian detection dataset and demonstrate improvements over two supervised baselines. Our first experiment extends Fast R-CNN and achieves a 4% relative improvement in test accuracy over its purely supervised baseline. Our second experiment extends Region Proposal Networks, achieving a 14% relative improvement in test accuracy.en
dc.description.statementofresponsibilityJerome Williams, Gustavo Carneiro, David Suteren
dc.language.isoenen
dc.publisherIEEEen
dc.rights©2017 IEEEen
dc.source.urihttps://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8226656en
dc.titleRegion of interest autoencoders with an application to pedestrian detectionen
dc.typeConference paperen
dc.identifier.rmid0030085943en
dc.contributor.conferenceInternational Conference on Digital Image Computing: Techniques and Applications (DICTA 2017) (29 Nov 2017 - 01 Dec 2017 : Sydney, AUSTRALIA)en
dc.identifier.doi10.1109/DICTA.2017.8227485en
dc.publisher.placePiscataway, NJen
dc.relation.granthttp://purl.org/au-research/grants/arc/LP130100521en
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100016en
dc.identifier.pubid406503-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS03en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]en
dc.identifier.orcidSuter, D. [0000-0001-6306-3023]en
Appears in Collections:Computer Science publications

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