Transferring a Semantic Representation for Person Re-Identification and Search

Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4184-4193

Abstract


Learning semantic attributes for person re-identification and description-based person search has gained increasing interest due to attributes' great potential as a pose and view-invariant representation. However, existing attribute-centric approaches have thus far underperformed state-of-the-art conventional approaches. This is due to their non-scalable need for extensive domain (camera) specific annotation. In this paper we present a new semantic attribute learning approach for person re-identification and search. Our model is trained on existing fashion photography datasets -- either weakly or strongly labelled. It can then be transferred and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision. The resulting representation is useful for both unsupervised and supervised person re-identification, achieving state-of-the-art and near state-of-the-art performance respectively. Furthermore, as a semantic representation it allows description-based person search to be integrated within the same framework.

Related Material


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[bibtex]
@InProceedings{Shi_2015_CVPR,
author = {Shi, Zhiyuan and Hospedales, Timothy M. and Xiang, Tao},
title = {Transferring a Semantic Representation for Person Re-Identification and Search},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}