Person Re-Identification by Deep Learning Attribute-Complementary Information

Arne Schumann, Rainer Stiefelhagen; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 20-28

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.

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[bibtex]
@InProceedings{Schumann_2017_CVPR_Workshops,
author = {Schumann, Arne and Stiefelhagen, Rainer},
title = {Person Re-Identification by Deep Learning Attribute-Complementary Information},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}