Person Re-Identification Based on Diversified Local Attention Network

XU Shengjun; LIU Qiuyuan; SHI Ya; MENG Yuebo; LIU Guanghui; HAN Jiuqiang

doi:10.11999/JEIT201003

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 211-220

XU Shengjun, LIU Qiuyuan, SHI Ya, MENG Yuebo, LIU Guanghui, HAN Jiuqiang. Person Re-Identification Based on Diversified Local Attention Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 211-220. doi: 10.11999/JEIT201003

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XU Shengjun, LIU Qiuyuan, SHI Ya, MENG Yuebo, LIU Guanghui, HAN Jiuqiang. Person Re-Identification Based on Diversified Local Attention Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 211-220. doi: 10.11999/JEIT201003

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Person Re-Identification Based on Diversified Local Attention Network

doi: 10.11999/JEIT201003

XU Shengjun^{1, 2},
LIU Qiuyuan^{1
,
,},
SHI Ya¹,
MENG Yuebo^{1, 2},
LIU Guanghui¹,
HAN Jiuqiang^{1, 2}

1.
School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
2.
AI & DE Guangdong Province Laboratory (GZ), Guangzhou 510320, China

Funds: The National Natural Science Foundation of China (61803293, 51678470), The Natural Science Basic Research Plan in Shaanxi Province of China (2020JM472, 2020JM473, 2019JQ760), Pa Zhou Laboratory Foundation (2020PZZZPT0002)

Received Date: 2020-11-30
Rev Recd Date: 2021-07-05

Available Online: 2021-08-19

Publish Date: 2022-01-10

Abstract

Abstract

To relieve the problem of occlusion and misalignment caused by pose/view variations in real world, a new deep architecture named Diversified Local Attention Network (DLAN) for person Re-IDentification (Re-ID) is proposed in this paper. On the whole, a global branch and multiple local attention branches are designed following the backbone network, which simultaneously learn the pedestrians' global spatial structure and salient local features of different body parts. Furthermore, a novel Consistent Activation Penalty (CAP) is devised to constraint the output of local networks so as to obtain the complementary and diversified feature representations. Finally, the global and local features are fed into the classification network to form more comprehensive description of pedestrian via jointly learning. Utilizing Market1501, DukeMTMC-reID and CUHK03 datasets, the proposed DLAN model has reached 88.4%/95.1%, 79.5%/88.7% and 74.3%/76.3% (mAP/Rank-1) respectively, which are better than the compared methods. The experiments adequately verify the robustness and discriminability of the proposed method.
- Person re-identification,
- Multiple local attention,
- Consistency Activation Penalty (CAP),
- Diversification

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References(32)

References

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