[1] Zhang Z Y, Ding J W, Wei H W et al. Cascaded multi-level features learning for attention based person re-identification[J]. Laser & Optoelectronics Progress, 58, 2215003(2021).

[2] Liu Z Y, Wan P P. Pedestrian re-identification feature extraction method based on attention mechanism[J]. Journal of Computer Applications, 40, 672-676(2020).

[3] Suh Y, Wang J D, Tang S Y et al. Part-aligned bilinear representations for person re-identification[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11218, 418-437(2018).

[4] Liu S, Dang J W, Wang S et al. Person re-identification based on first-order and second-order spatial information[J]. Laser & Optoelectronics Progress, 58, 0215005(2021).

[5] Wu K, Zhang B H, Lü X Q et al. Person re-identification based on squeeze and excitation residual neural network and feature fusion[J]. Laser & Optoelectronics Progress, 57, 181007(2020).

[6] Li C, Jiang M, Kong J. Multi-branch person re-identification based on multi-scale attention[J]. Laser & Optoelectronics Progress, 57, 201001(2020).

[7] Li W, Zhu X T, Gong S G. Harmonious attention network for person re-identification[C], 2285-2294(2018).

[8] Sun Y F, Zheng L, Yang Y et al. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline)[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11208, 501-518(2018).

[9] Wang G S, Yuan Y F, Chen X et al. Learning discriminative features with multiple granularities for person re-identification[C], 274-282(2018).

[10] Ghiasi G, Lin T Y, Le Q V. DropBlock: a regularization method for convolutional networks[C], 10727-10737(2018).

[11] Dai Z Z, Chen M Q, Gu X D et al. Batch DropBlock network for person re-identification and beyond[C], 3690-3700(2019).

[12] Pan X G, Luo P, Shi J P et al. Two at once: enhancing learning and generalization capacities via IBN-net[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11208, 484-500(2018).

[13] Zheng Z D, Zheng L, Yang Y. A discriminatively learned CNN embedding for person reidentification[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 14, 1-20(2018).

[14] Schroff F, Kalenichenko D, Philbin J. FaceNet: a unified embedding for face recognition and clustering[C], 815-823(2015).

[15] Xiong W, Yang D C, Xiong Z J et al. Person re-identification algorithm based on global feature stitching[J]. Application Research of Computers, 38, 316-320(2021).

[16] Zheng L, Shen L Y, Tian L et al. Scalable person re-identification: a benchmark[C], 1116-1124(2015).

[17] Ristani E, Solera F, Zou R et al. Performance measures and a data set for multi-target, multi-camera tracking[M]. Hua G, Jégou H. Computer vision-ECCV 2016 workshops, 9914, 17-35(2016).

[18] Li W, Zhao R, Xiao T et al. DeepReID: deep filter pairing neural network for person re-identification[C], 152-159(2014).

[19] Miao J X, Wu Y, Liu P et al. Pose-guided feature alignment for occluded person re-identification[C], 542-551(2019).

[20] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision[C], 2818-2826(2016).

[21] DeVries T, Taylor G W. Improved regularization of convolutional neural networks with cutout[EB/OL]. https://arxiv.org/abs/1708.04552

[22] Zhong Z, Zheng L, Kang G L et al. Random erasing data augmentation[EB/OL]. https://arxiv.org/abs/1708.04896

[23] Zheng L, Yang Y, Hauptmann A G. Person re-identification: past, present and future[EB/OL]. https://arxiv.org/abs/1610.02984

[24] Sun Y F, Zheng L, Deng W J et al. SVDNet for pedestrian retrieval[C], 3820-3828(2017).

[25] Hou R B, Ma B P, Chang H et al. Interaction-and-aggregation network for person re-identification[C], 9309-9318(2019).

[26] Zhong Z, Zheng L, Cao D L et al. re-ranking person re-identification with k-reciprocal encoding[C], 3652-3661(2017).

[27] Zhao L M, Li X, Zhuang Y T et al. Deeply-learned part-aligned representations for person re-identification[C], 3239-3248(2017).

[28] He L X, Sun Z N, Zhu Y H et al. Recognizing partial biometric patterns[EB/OL]. https://arxiv.org/abs/1810.07399

[29] Wang G A, Yang S, Liu H Y et al. High-order information matters: learning relation and topology for occluded person re-identification[C], 6448-6457(2020).