Person Re-identification Based on Multi-scale Network Attention Fusion

Fenhua WANG; Bo ZHAO; Chao HUANG; Youqi YAN

doi:10.11999/JEIT190998

Volume 42 Issue 12

Dec. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(12): 3045-3052

Fenhua WANG, Bo ZHAO, Chao HUANG, Youqi YAN. Person Re-identification Based on Multi-scale Network Attention Fusion[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3045-3052. doi: 10.11999/JEIT190998

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Fenhua WANG, Bo ZHAO, Chao HUANG, Youqi YAN. Person Re-identification Based on Multi-scale Network Attention Fusion[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3045-3052. doi: 10.11999/JEIT190998

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Person Re-identification Based on Multi-scale Network Attention Fusion

doi: 10.11999/JEIT190998

Fenhua WANG^{1, 2, 3
,
,},
Bo ZHAO¹,
Chao HUANG¹,
Youqi YAN¹

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China
3.
Beijing Engineering Research Center of Industrial Spectrum Imaginghe, Beijing 100083, China

Funds: The Key Projects of National Key R & D Plan (2017YFB1400101-01), Beijing University of Science and Technology Central University Basic Research Business Expenses (FRF-BD-19-002A)

Received Date: 2019-12-13
Rev Recd Date: 2020-06-17

Available Online: 2020-07-20

Publish Date: 2020-12-08

Abstract

Abstract

The key to person re-identification depends on the extraction of pedestrian characteristics. Convolutional neural networks have powerful feature extraction and expression capabilities. In view of the fact that different features can be observed at different scales, a pedestrian re-identification method based on Multi-Scale Attention Network(MSAN) fusion is proposed. This method samples the features at different depths of the network and fuses the sampled features to predict pedestrians. Feature maps of different depths have different expressive powers, enabling the network to learn more fine-grained features of pedestrians. At the same time, the attention module is embedded in the residual network, so that the network can pay more attention to some key information and enhance the network feature learning ability. The accuracy of the proposed method on the datasets such as Market1501, DukeMTMC-reID and MSMT17_V1 reaches 95.3%, 89.8% and 82.2%, respectively. Experiments show that the method makes full use of the information of different depths of the network and the key information of interest, so that the model has strong discriminating ability, and the average accuracy of the proposed model is better than most state-of-the-art algorithms.
- Person re-identification,
- Multiple scale,
- Attention,
- Residual network,
- Metric learning

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References(25)

References

FARENZENA M, BAZZANI L, PERINA A, et al. Person re-identification by symmetry-driven accumulation of local features[C]. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010: 2360–2367.

周智恒, 刘楷怡, 黄俊楚, 等. 一种基于等距度量学习策略的行人重识别改进算法[J]. 电子与信息学报, 2019, 41(2): 477–483. doi: 10.11999/JEIT180336

ZHOU Zhiheng, LIU Kaiyi, HUANG Junchu, et al. Improved metric learning algorithm for person re-identification based on equidistance[J]. Journal of Electronics &Information Technology, 2019, 41(2): 477–483. doi: 10.11999/JEIT180336

HIRZER M, ROTH P M, KÖSTINGER M, et al. Relaxed pairwise learned metric for person re-identification[C]. The 12th European Conference on Computer Vision, Florence, Italy, 2012: 780–793.

SUN Yifan, ZHENG Liang, YANG Yi, et al. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)[C]. The 15th European Conference on Computer Vision (ECCV), Munich, Germany, 2018: 480–496.

LUO Hao, JIANG Wei, ZHANG Xuan, et al. AlignedReID++: Dynamically matching local information for person re-identification[J]. Pattern Recognition, 2019, 94: 53–61. doi: 10.1016/j.patcog.2019.05.028

WANG Guanshuo, YUAN Yufeng, CHEN Xiong, et al. Learning discriminative features with multiple granularities for person re-identification[C]. 2018 ACM Multimedia Conference on Multimedia Conference, Seoul, Korea, 2018: 274–282.

陈鸿昶, 吴彦丞, 李邵梅, 等. 基于行人属性分级识别的行人再识别[J]. 电子与信息学报, 2019, 41(9): 2239–2246. doi: 10.11999/JEIT180740

CHEN Hongchang, WU Yancheng, LI Shaomei, et al. Person re-identification based on attribute hierarchy recognition[J]. Journal of Electronics &Information Technology, 2019, 41(9): 2239–2246. doi: 10.11999/JEIT180740

DAI Zuozhuo, CHEN Mingqiang, GU Xiaodong, et al. Batch DropBlock network for person re-identification and beyond[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 3691–3701.

WOO S, PARK J, LEE J Y, et al. Cbam: Convolutional block attention module[C]. The 15th European Conference on Computer Vision (ECCV), Munich, Germany, 2018: 3–19.

LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]. 2017 IEEE conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2117–2125.

HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778.

HERMANS A, BEYER L, and LEIBE B. In defense of the triplet loss for person re-identification[EB/OL]. https://arxiv.org/abs/1703.07737, 2017.

ZHENG Liang, SHEN Liyue, TIAN Lu, et al. Scalable person re-identification: A benchmark[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1116–1124.

RISTANI E, SOLERA F, ZOU R, et al. Performance measures and a data set for multi-target, multi-camera tracking[C]. 2016 European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 17–35.

WEI Longhui, ZHANG Shiliang, GAO Wen, et al. Person transfer GAN to bridge domain gap for person re-identification[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 79–88.

ZHONG Zhun, ZHENG Liang, CAO Donglin, et al. Re-ranking person re-identification with k-reciprocal encoding[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 1318–1327.

SALLEH S S, AZIZ N A A, MOHAMAD D, et al. Combining mahalanobis and jaccard distance to overcome similarity measurement constriction on geometrical shapes[J]. International Journal of Computer Science Issues, 2012, 9(4): 124–132.

ZHENG Zhedong, YANG Xiaodong, YU Zhiding, et al. Joint discriminative and generative learning for person re-identification[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 2138–2147.

HOU Ruibing, MA Bingpeng, CHANG Hong, et al. Interaction-and-aggregation network for person re-identification[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 9317–9326.

ZHOU Kaiyang, YANG Yongxin, CAVALLARO A, et al. Omni-Scale feature learning for person re-identification[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 3702–3712.

SUN Yifan, ZHENG Liang, DENG Weijian, et al. SVDNet for pedestrian retrieval[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 3800–3808.

CHEN Yanbei, ZHU Xiatian, and GONG Shaogang. Person re-identification by deep learning multi-scale representations[C]. 2017 IEEE International Conference on Computer Vision Workshops, Venice, Italy, 2017: 2590–2600.

ZHONG Zhun, ZHENG Liang, KANG Guoliang, et al. Random erasing data augmentation[EB/OL]. https://arxiv.org/abs/1708.04896, 2017.

WANG Yan, WANG Lequn, YOU Yurong, et al. Resource aware person re-identification across multiple resolutions[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 8042–8051.

ALMAZAN J, GAJIC B, MURRAY N, et al. Re-ID done right: towards good practices for person re-identification[EB/OL]. https://arxiv.org/abs/1801.05339, 2018.

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