RCAA: Relational context-aware agents for person search

Chang, X; Huang, PY; Shen, YD; Liang, X; Yang, Y; Hauptmann, AG

RCAA: Relational context-aware agents for person search

Chang, X Huang, PY Shen, YD Liang, X Yang, Y

Hauptmann, AG

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11213 LNCS pp. 86 - 102
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Chang, X	en_US
dc.contributor.author	Huang, PY	en_US
dc.contributor.author	Shen, YD	en_US
dc.contributor.author	Liang, X	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Hauptmann, AG	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11213 LNCS pp. 86 - 102	en_US
dc.identifier.isbn	9783030012397	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131485
dc.description.abstract	© Springer Nature Switzerland AG 2018. We aim to search for a target person from a gallery of whole scene images for which the annotations of pedestrian bounding boxes are unavailable. Previous approaches to this problem have relied on a pedestrian proposal net, which may generate redundant proposals and increase the computational burden. In this paper, we address this problem by training relational context-aware agents which learn the actions to localize the target person from the gallery of whole scene images. We incorporate the relational spatial and temporal contexts into the framework. Specifically, we propose to use the target person as the query in the query-dependent relational network. The agent determines the best action to take at each time step by simultaneously considering the local visual information, the relational and temporal contexts, together with the target person. To validate the performance of our approach, we conduct extensive experiments on the large-scale Person Search benchmark dataset and achieve significant improvements over the compared approaches. It is also worth noting that the proposed model even performs better than traditional methods with perfect pedestrian detectors.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-030-01240-3_6	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	RCAA: Relational context-aware agents for person search	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11213 LNCS	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	11213 LNCS	en_US

Abstract:

© Springer Nature Switzerland AG 2018. We aim to search for a target person from a gallery of whole scene images for which the annotations of pedestrian bounding boxes are unavailable. Previous approaches to this problem have relied on a pedestrian proposal net, which may generate redundant proposals and increase the computational burden. In this paper, we address this problem by training relational context-aware agents which learn the actions to localize the target person from the gallery of whole scene images. We incorporate the relational spatial and temporal contexts into the framework. Specifically, we propose to use the target person as the query in the query-dependent relational network. The agent determines the best action to take at each time step by simultaneously considering the local visual information, the relational and temporal contexts, together with the target person. To validate the performance of our approach, we conduct extensive experiments on the large-scale Person Search benchmark dataset and achieve significant improvements over the compared approaches. It is also worth noting that the proposed model even performs better than traditional methods with perfect pedestrian detectors.

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http://hdl.handle.net/10453/131485