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Person re-identification by deep learning attribute-complementary information

 
: Schumann, A.; Stiefelhagen, R.

:
Postprint urn:nbn:de:0011-n-4707091 (3.4 MByte PDF)
MD5 Fingerprint: 66df675ba91351bc9b6f34042b227033
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Created on: 8.2.2018


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017. Proceedings : 21 - 26 July 2016, Honolulu, Hawaii
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2017
ISBN: 978-1-5386-0734-3
ISBN: 978-1-5386-0733-6
pp.1435-1443
Conference on Computer Vision and Pattern Recognition Workshops (CVPR) <30, 2017, Honolulu/Hawaii>
Workshop on Target Re-Identification and Multi-Target Multi-Camera Tracking <1, 2017, Honolulu/Hawaii>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Abstract
Automatic person re-identification (re-id) across camera boundaries is a challenging problem. Approaches have to be robust against many factors which influence the visual appearance of a person but are not relevant to the person's identity. Examples for such factors are pose, camera angles, and lighting conditions. Person attributes are a semantic high level information which is invariant across many such influences and contain information which is often highly relevant to a person's identity. In this work we develop a re-id approach which leverages the information contained in automatically detected attributes. We train an attribute classifier on separate data and include its responses into the training process of our person re-id model which is based on convolutional neural networks (CNNs). This allows us to learn a person representation which contains information complementary to that contained within the attributes. Our approach is able to identify attributes which perform most reliably for re-id and focus on them accordingly. We demonstrate the performance improvement gained through use of the attribute information on multiple large-scale datasets and report insights into which attributes are most relevant for person re-id.

: http://publica.fraunhofer.de/documents/N-470709.html