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Retrieving relative soft biometrics for semantic identification

Retrieving relative soft biometrics for semantic identification
Retrieving relative soft biometrics for semantic identification
Automatically describing pedestrians in surveillance footage is crucial to facilitate human accessible solutions for suspect identification. We aim to identify pedestrians based solely on human description, by automatically retrieving semantic attributes from surveillance images, alleviating exhaustive label annotation. This work unites a deep learning solution with relative soft biometric labels, to accurately retrieve more discriminative image attributes. We propose a Semantic Retrieval Convolutional Neural Network to investigate automatic retrieval of three soft biometric modalities, across a number of 'closed-world' and 'open-world' re-identification scenarios. Findings suggest that relative-continuous labels are more accurately predicted than absolute-binary and relative-binary labels, improving semantic identification in every scenario. Furthermore, we demonstrate a top rank-1 improvement of 23.2% and 26.3% over a traditional, baseline retrieval approach, in one-shot and multi-shot re-identification scenarios respectively.
IEEE
Martinho-Corbishley, Daniel
6dd73e5c-9a7e-41bd-b896-fb1ea9852abb
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Martinho-Corbishley, Daniel
6dd73e5c-9a7e-41bd-b896-fb1ea9852abb
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da

Martinho-Corbishley, Daniel, Nixon, Mark and Carter, John (2016) Retrieving relative soft biometrics for semantic identification. In, 2016 23rd International Conference on Pattern Recognition (ICPR). 23rd International Conference on Pattern Recognition (ICPR'16) (08/12/16) IEEE. (doi:10.1109/ICPR.2016.7900105).

Record type: Book Section

Abstract

Automatically describing pedestrians in surveillance footage is crucial to facilitate human accessible solutions for suspect identification. We aim to identify pedestrians based solely on human description, by automatically retrieving semantic attributes from surveillance images, alleviating exhaustive label annotation. This work unites a deep learning solution with relative soft biometric labels, to accurately retrieve more discriminative image attributes. We propose a Semantic Retrieval Convolutional Neural Network to investigate automatic retrieval of three soft biometric modalities, across a number of 'closed-world' and 'open-world' re-identification scenarios. Findings suggest that relative-continuous labels are more accurately predicted than absolute-binary and relative-binary labels, improving semantic identification in every scenario. Furthermore, we demonstrate a top rank-1 improvement of 23.2% and 26.3% over a traditional, baseline retrieval approach, in one-shot and multi-shot re-identification scenarios respectively.

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More information

Accepted/In Press date: 14 July 2016
e-pub ahead of print date: December 2016
Venue - Dates: 23rd International Conference on Pattern Recognition (ICPR'16), Mexico, 2016-12-04 - 2016-12-08
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 402985
URI: http://eprints.soton.ac.uk/id/eprint/402985
PURE UUID: bf873626-37db-4d5e-95f5-7ff28a112f41
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 21 Nov 2016 14:20
Last modified: 10 Dec 2019 01:59

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