A Multi-Task Deep Network for Person Re-Identification

Authors

  • Weihua Chen Institute of Automation, Chinese Academy of Sciences
  • Xiaotang Chen Institute of Automation, Chinese Academy of Sciences
  • Jianguo Zhang University of Dundee
  • Kaiqi Huang Institute of Automation, Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v31i1.11201

Keywords:

Person Re-identification, Multi-task, Deep learning

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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Published

2017-02-12

How to Cite

Chen, W., Chen, X., Zhang, J., & Huang, K. (2017). A Multi-Task Deep Network for Person Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11201