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A Multi-task Deep Network for Person Re-identification

A Multi-task Deep Network for Person Re-identification

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Original languageEnglish
Title of host publicationProceedings of The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
EditorsSatinder Singh, Shaul Markovitch
Place of PublicationPalo Alto, CA
PublisherAAAI Press
Pages3988-3994
Number of pages7
StatePublished - 17 Feb 2017
EventThirty-first AAAI Conference on Artificial Intelligence - San Francisco, United States

Conference

ConferenceThirty-first AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-17
CountryUnited States
CitySan Francisco
Period4/02/179/02/17
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Abstract

Person re-identification (ReID) focuses on identifying people across different scenes in video surveillance, which is usually formulated as a binary classification task or a ranking
task in current person ReID approaches. In this paper, we take both tasks into account and propose a multi-task deep network (MTDnet) that makes use of their own advantages
and jointly optimize the two tasks simultaneously for person ReID. To the best of our knowledge, we are the first to integrate both tasks in one network to solve the person ReID.
We show that our proposed architecture significantly boosts the performance. Furthermore, deep architecture in general requires a sufficient dataset for training, which is usually not met in person ReID. To cope with this situation, we further extend the MTDnet and propose a cross-domain architecture that is capable of using an auxiliary set to assist training on small target sets. In the experiments, our approach outperforms most of existing person ReID algorithms on representative datasets including CUHK03, CUHK01, VIPeR, iLIDS and PRID2011, which clearly demonstrates the effectiveness of the proposed approach.

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