Person Re-identification Based on Attribute Hierarchy Recognition

Hongchang CHEN; Yancheng WU; Shaomei LI; Chao GAO

doi:10.11999/JEIT180740

Volume 41 Issue 9

Sep. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(9): 2239-2246

Hongchang CHEN, Yancheng WU, Shaomei LI, Chao GAO. Person Re-identification Based on Attribute Hierarchy Recognition[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2239-2246. doi: 10.11999/JEIT180740

Citation:

Hongchang CHEN, Yancheng WU, Shaomei LI, Chao GAO. Person Re-identification Based on Attribute Hierarchy Recognition[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2239-2246. doi: 10.11999/JEIT180740

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Person Re-identification Based on Attribute Hierarchy Recognition

doi: 10.11999/JEIT180740

China National Digital Switching System Engineering & Technological R&D Center, Zhengzhou 450002, China

Funds: The National Natural Science Foundation of China (61601513)

Received Date: 2018-07-20
Rev Recd Date: 2019-03-03

Available Online: 2019-04-17

Publish Date: 2019-09-10

Abstract

Abstract

In order to improve the accuracy rate of person re-identification, a pedestrian attribute hierarchy recognition neural network is proposed based on attention model. Compared with the existing algorithms, the model has the following three advantages. Firstly, the attention model is used in this paper to identify the pedestrian attributes, and to extract of pedestrian attribute information and degree of significance. Secondly, the attention model in used in this paper to classify the attributes according to the significance of the pedestrian attributes and the amount of informationcontained. Thirdly, this paper analyzes the correlation between attributes, and adjusts the next level identification strategy according to the recognition results of the upper level. It can improve the recognition accuracy of small target attributes, and the accuracy of pedestrian recognition is improved. The experimental results show that the proposed model can effectively improve the first accuracy rate (rank-1) of person re-identification compared with the existing methods. On the Market1501 dataset, the first accuracy rate is 93.1%, and the first accuracy rate is 81.7% on the DukeMTMC dataset.
- Person re-identification,
- Attention model,
- Deep learning,
- Saliency,
- Hierarchy

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References(20)

References

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