Learning multi-view deep features for small object retrieval in surveillance scenarios

Guo, H; Wang, J; Xu, M; Zha, ZJ; Lu, H

Learning multi-view deep features for small object retrieval in surveillance scenarios

Guo, H Wang, J Xu, M

Zha, ZJ Lu, H

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Publication Type:: Conference Proceeding
Citation:: MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, 2015, pp. 859 - 862
Issue Date:: 2015-10-13

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Field	Value	Language
dc.contributor.author	Guo, H	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Zha, ZJ	en_US
dc.contributor.author	Lu, H	en_US
dc.date.issued	2015-10-13	en_US
dc.identifier.citation	MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, 2015, pp. 859 - 862	en_US
dc.identifier.isbn	9781450334594	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121740
dc.description.abstract	© 2015 ACM. With the explosive growth of surveillance videos, object re-trieval has become a significant task for security monitoring. However, visual objects in surveillance videos are usually of small size with complex light conditions, view changes and partial occlusions, which increases the dificulty level of eff-ciently retrieving objects of interest in a large-scale dataset. Although deep features have achieved promising results on object classification and retrieval and have been veriffed to contain rich semantic structure property, they lack of ade-quate color information, which is as crucial as structure in-formation for effective object representation. In this paper, we propose to leverage discriminative Convolutional Neural Network (CNN) to learn deep structure and color feature to form an Effcient multi-view object representation. Specifi-cally, we utilize CNN trained on ImageNet to abstract rich semantic structure information. Meanwhile, we propose a CNN model supervised by 11 color names to extract deep color features. Compared with traditional color descriptors, deep color features can capture the common color property across difierent illumination conditions. Then, the comple-mentary multi-view deep features are encoded into short bi-nary codes by Locality-Sensitive Hash (LSH) and fused to retrieve objects. Retrieval experiments are performed on a dataset of 100k objects extracted from multi-camera surveil-lance videos. Comparison results with several popular visual descriptors show the effectiveness of the proposed approach.	en_US
dc.relation.ispartof	MM 2015 - Proceedings of the 2015 ACM Multimedia Conference	en_US
dc.relation.isbasedon	10.1145/2733373.2806349	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Learning multi-view deep features for small object retrieval in surveillance scenarios	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2015 ACM. With the explosive growth of surveillance videos, object re-trieval has become a significant task for security monitoring. However, visual objects in surveillance videos are usually of small size with complex light conditions, view changes and partial occlusions, which increases the dificulty level of eff-ciently retrieving objects of interest in a large-scale dataset. Although deep features have achieved promising results on object classification and retrieval and have been veriffed to contain rich semantic structure property, they lack of ade-quate color information, which is as crucial as structure in-formation for effective object representation. In this paper, we propose to leverage discriminative Convolutional Neural Network (CNN) to learn deep structure and color feature to form an Effcient multi-view object representation. Specifi-cally, we utilize CNN trained on ImageNet to abstract rich semantic structure information. Meanwhile, we propose a CNN model supervised by 11 color names to extract deep color features. Compared with traditional color descriptors, deep color features can capture the common color property across difierent illumination conditions. Then, the comple-mentary multi-view deep features are encoded into short bi-nary codes by Locality-Sensitive Hash (LSH) and fused to retrieve objects. Retrieval experiments are performed on a dataset of 100k objects extracted from multi-camera surveil-lance videos. Comparison results with several popular visual descriptors show the effectiveness of the proposed approach.

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http://hdl.handle.net/10453/121740