| Citation: | ZHANG Zihao, ZHOU Qianli, WANG Ronget al. Pedestrian re-identification method based on spatial attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1747-1755. doi: 10.13700/j.bh.1001-5965.2020.0075(in Chinese)
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Pedestrian re-identification has always been an important part of image retrieval. However, due to different pedestrian poses and complex backgrounds, the extracted pedestrian features are not robust and representative, which in turn affects the accuracy of pedestrian re-recognition. In this paper, based on AlignedReID++ algorithm, we proposes a pedestrian re-identification method based on spatial attention mechanism. First, in the feature extraction part, a spatial attention mechanism is introduced to enhance feature expression while suppressing possible noise. Second, the Instance-Normalization (IN) layer is introduced in the convolution layer to assist the Batch-Normalization (BN) layer to normalize the features and to solve the problem of single BN layer insensitivity to feature tonal and illumination changes, which enhances the robustness of feature extraction to tonal and illumination changes. Finally, to validate the proposed method, extensive experiment has been carried out on the Market1501, DukeMTMC, and CUHK03 pedestrian re-identification datasets. The experimental results show that the recognition accuracy of the improved model on the three datasets has been improved by 2%, 2.9%, and 5.1%, respectively, compared with model before modification, which indicates that the proposed method achieves higher accuracy and more robustness.
| [1] |
LIAO S C, HU Y, ZHU X Y, et al.Person re-identification by local maximal occurrence representation and metric learning[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2015: 2197-2206.
|
| [2] |
DE MAESSCHALCK R, JOUAN-RIMBAUD D, MASSART D L.The Mahalanobis distance[J].Chemometrics and Intelligent Laboratory Systems, 2000, 50(1):1-18.
|
| [3] |
YI D, LEI Z, LIAO S C, et al.Deep metric learning for person re-identification[C]//2014 22nd International Conference on Pattern Recognition.Piscataway: IEEE Press, 2014: 34-39.
|
| [4] |
LI W, ZHAO R, XIAO T, et al.DeepReID: Deep filter pairing neural network for person re-identification[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2014: 152-159.
|
| [5] |
VARIOR R R, SHUAI B, LU J, et al.A Siamese long short-term memory architecture for human re-identification[C]//European Conference on Computer Vision.Berlin: Springer, 2016: 135-153.
|
| [6] |
ZHANG X, LUO H, FAN X, et al.AlignedReID: Surpassing human-level performance in person re-identification[EB/OL].(2018-01-31)[2020-03-02].
|
| [7] |
DENG W, ZHENG L, YE Q, et al.Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification[C]//2018 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2018: 994-1003.
|
| [8] |
LUO H, JIANG W, ZHANG X, 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
|
| [9] |
HERMANS A, BEYER L, LEIBE B.In defense of the triplet loss for person re-identification[EB/OL].(2017-11-21)[2020-03-02].
|
| [10] |
IOFFE S, SZEGEDY C.Batch normalization: Accelerating deep network training by reducing internal covariate shift[EB/OL].(2015-03-02)[2020-03-02].
|
| [11] |
LI X, HU X, YANG J.Spatial group-wise enhance: Improving semantic feature learning in convolutional networks[EB/OL].(2019-05-25)[2020-03-02].
|
| [12] |
PAN X, LUO P, SHI J, et al.Two at once: Enhancing learning and generalization capacities via IBN-Net[EB/OL].(2018-07-27)[2020-03-02].
|
| [13] |
XIAO Q, LUO H, ZHANG C.Margin sample mining loss: A deep learning based method for person re-identification[EB/OL].(2017-10-07)[2020-03-02].http://arxiv.org/abs/1710.00478.
|
| [14] |
ZHENG L, YANG Y, HAUPTMANN A G.Person re-identification: Past, present and future[EB/OL].(2016-10-10)[2020-03-02].
|
| [15] |
LIU H, FENG J S, QI M B, et al.End-to-end comparative attention networks for person re-identification[J].IEEE Transactions on Image Processing, 2017, 26(7):3492-3506. doi: 10.1109/TIP.2017.2700762
|
| [16] |
CHENG D, GONG Y H, ZHOU S P, et al.Person re-identification by multi-channel parts-based CNN with improved triplet loss function[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2016: 1335-1344.
|
| [17] |
杜鹏, 宋永红, 张鑫瑶.基于自注意力模态融合网络的跨模态行人再识别方法研究[J/OL].自动化学报: 1-12(2019-10-16)[2020-01-06].
DU P, SONG Y H, ZHANG X Y.Self-attention cross-modality fusion network for cross-modality person re-identification[J/OL].Acta Automatica Sinica: 1-12(2019-10-16)[2020-01-06].in Chinese).
|
| [18] |
张丽红, 孙志琳.基于多层深度特征融合的行人再识别研究[J].测试技术学报, 2018, 32(4):48-52.
ZHANG L H, SUN Z L.Person re-identification based on multi-layer deep feature fusion[J].Journal of Test and Measurement Technology, 2018, 32(4):48-52(in Chinese).
|
| [19] |
李鹏, 王德勇, 师文喜, 等.大数据环境下基于深度学习的行人再识别[J].北京邮电大学学报, 2019, 42(6):29-34.
LI P, WANG D Y, SHI W X, et al.Research on person re-identification based on deep learning under big data environment[J].Journal of Beijing University of Posts and Telecommunications, 2019, 42(6):29-34(in Chinese).
|
| [20] |
SUN Y, ZHENG L, DENG W, et al.SVDNet for pedestrian retrieval[C]//2017 IEEE International Conference on Computer Vision (ICCV).Piscataway: IEEE Press, 2017: 3820-3828.
|
| [21] |
SARFRAZ M S, SCHUMANN A, EBERLE A, et al.A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking[EB/OL].(2018-04-05)[2020-03-02].
|
| [22] |
AN L, QIN Z, CHEN X J, et al.Multi-level common space learning for person re-identification[J].IEEE Transactions on Circuits & Systems for Video Technology, 2018, 28(8):1777-1787.
|
| [23] |
LI W, ZHU X, GONG S.Harmonious attention network for person re-identification[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE Press, 2018: 2285-2294.
|
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