[1] Luo H, Jiang W, Fan X et al. A survey on deep learning based person re-identification[J]. Acta Automatica Sinica, 45, 2032-2049(2019).

[2] Ahmed E, Jones M, Marks T K. An improved deep learning architecture for person re-identification[C], 3908-3916(2015).

[3] Zhang T, Yi Z M, Li X et al. Improved algorithm for person re-identification based on global features[J]. Laser & Optoelectronics Progress, 57, 241503(2020).

[4] 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. Lecture notes in computer science, 11208, 501-518(2018).

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

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

[7] Chen X S, Fu C M, Zhao Y et al. Salience-guided cascaded suppression network for person re-identification[C], 3297-3307(2020).

[8] 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).

[9] Tian M Q, Yi S, Li H S et al. Eliminating background-bias for robust person re-identification[C], 5794-5803(2018).

[10] Song C F, Huang Y, Ouyang W L et al. Mask-guided contrastive attention model for person re-identification[C], 1179-1188(2018).

[11] Qi L, Huo J, Wang L et al. A mask based deep ranking neural network for person retrieval[C], 496-501(2019).

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

[13] 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. Lecture notes in computer science, 9914, 17-35(2016).

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

[15] Wei L H, Zhang S L, Gao W et al. Person transfer GAN to bridge domain gap for person re-identification[C], 79-88(2018).

[16] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[17] Felzenszwalb P, McAllester D, Ramanan D. A discriminatively trained, multiscale, deformable part model[C](2008).

[18] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[19] Tay C P, Roy S, Yap K H. AANet: attribute attention network for person re-identifications[C], 7127-7136(2019).

[20] Zheng F, Deng C, Sun X et al. Pyramidal person re-identification via multi-loss dynamic training[C], 8506-8514(2019).

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

[22] Zhang Z Z, Lan C L, Zeng W J et al. Densely semantically aligned person re-identification[C], 667-676(2019).

[23] Yang W J, Huang H J, Zhang Z et al. Towards rich feature discovery with class activation maps augmentation for person re-identification[C], 1389-1398(2019).

[24] Zhou K Y, Yang Y X, Cavallaro A et al. Omni-scale feature learning for person re-identification[C], 3701-3711(2019).

[25] Chen B H, Deng W H, Hu J N. Mixed high-order attention network for person re-identification[C], 371-381(2019).

[26] He L X, Wang Y G, Liu W et al. Foreground-aware pyramid reconstruction for alignment-free occluded person re-identification[C], 8449-8458(2019).

[27] Quan R J, Dong X Y, Wu Y et al. Auto-ReID: searching for a part-aware ConvNet for person re-identification[C], 3749-3758(2019).

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

[29] Zheng Z D, Yang X D, Yu Z D et al. Joint discriminative and generative learning for person re-identification[C], 2133-2142(2019).