[1] [1] LIAO S C, HU Y, ZHU X Y, et al. Person re-identification by local maximal occurrence representation and metric learning ［C］//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA: IEEE, 2015: 2197-2206.

[2] [2] CHEN L Y, LI W J. Multishape part network architecture for person re-identification ［J］. Journal of Image and Graphics, 2019, 24(11): 1932-1941. (in Chinese)

[3] [3] JIANG J G,YANG N,QI M B, et al. Person re-identification with region block segmentation and fusion ［J］．Journal of Image and Graphics, 2019, 24(4): 513-522. (in Chinese)

[4] [4] ZHU F Q, KONG X W, FU H Y, et al. Two-stream complementary symmetrical CNN architecture for person re-identification ［J］．Journal of Image and Graphics, 2018, 23(7): 1052-1060. (in Chinese)

[5] [5] CHEN B, ZHA Y F, LI Y Q, et al. Person re-identification based on convolutional neural network discriminative feature learning ［J］. Acta Optica Sinica, 2018, 38(7): 0720001. (in Chinese)

[6] [6] ZHENG L, YANG Y, HAUPTMANN A G. Person re-identification: past, present and future ［J］. arXiv, 2016: 1610.02984.

[7] [7] LIN Y T, ZHENG L, ZHENG Z D, et al. Improving person re-identification by attribute and identity learning ［J］. ArXiv, 2017: 1703.07220.

[8] [8] 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］//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV: IEEE, 2016: 1335-1344.

[9] [9] HERMANS A, BEYER L, LEIBE B. In defense of the triplet loss for person re-identification ［J］. ArXiv, 2017: 1703.07737.

[10] [10] SUN Y F, XU Q, LI Y L, et al. Perceive where to focus: learning visibility-aware part-level features for partial person re-identification ［J］. ArXiv, 2019: 1904.00537.

[11] [11] VARIOR R R, SHUAI B, LU J W, et al. A Siamese long short-term memory architecture for human re-identification ［C］//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016: 135-153.

[12] [12] SUN Y F, ZHENG L, YANG Y et al. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline) ［C］//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer, 2018: 501-518.

[13] [13] ZHANG X, LUO H, FAN X, et al. AlignedReID: surpassing human-level performance in person re–identification ［J］. ArXiv, 2017: 1711.08184.

[14] [14] ZHENG L, HUANG Y J, LU H C, et al. Pose invariant embedding for deep person re-identification ［J］. ArXiv, 2017: 1701.07732.

[15] [15] ZHENG Z D, ZHENG L, YANG Y. Pedestrian alignment network for large-scale person re-identification ［J］. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 29(10): 3037-3045.

[16] [16] SARFRAZ M S, SCHUMANN A, EBERLE A, et al. A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking ［C］//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE, 2018: 420-429.

[17] [17] SU H Y, WANG J H, TANG S Y, et al. Part-aligned bilinear representations for person re-identification ［C］//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer, 2018: 418-437.

[18] [18] ZHAO H Y, TIAN M Q, SUN S Y, et al. Spindle net: person re-identification with human body region guided feature decomposition and fusion ［C］//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI: IEEE, 2017: 907-915.

[19] [19] WEI L H, ZHANG S L, YAO H T, et al. GLAD: global-local-alignment descriptor for scalable person re-identification ［J］. IEEE Transactions on Multimedia, 2019, 21(4): 986-999.

[20] [20] SU C, LI J N, ZHANG S L, et al. Pose-driven deep convolutional model for person re-identification ［C］//Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 3980-3989.

[21] [21] LI W, ZHU X T, GONG S G. Harmonious attention network for person re-identification ［C］//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE, 2018: 2285-2294.

[22] [22] CHEN Y B, ZHU X T, GONG S G, et al. Person re-identification by deep learning multi-scale representations ［C］//Proceedings of 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). Venice: IEEE, 2017: 2590-2600.

[23] [23] WANG Y, WANG L Q, YOU Y R et al. Resource aware person re-identification across multiple resolutions ［C］//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE, 2018: 8042-8051.

[24] [24] YUN W G, SHI Q Q, WANG M. Multi-feature fusion gesture recognition based on deep convolutional neural networks ［J］. Chinese Journal of Liquid Crystals and Displays, 2019, 34(4): 417-422. (in Chinese)

[25] [25] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition ［C］//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV: IEEE, 2016: 770-778.

[26] [26] HERMANS A, BEYER L, LEIBE B. In defense of the triplet loss for person re-identification ［J］. ArXiv, 2017: 1703.07737.

[27] [27] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision ［C］//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV: IEEE, 2016: 2818-2826.

[28] [28] ZHONG Z, ZHENG L, KANG G, et al. Random erasing data augmentation ［J］. ArXiv, 2017: 1708.04896.

[29] [29] ZHAO Q H, ZHANG H Y, LI Y. Target extraction from LiDAR point cloud data using irregular geometry marked point process ［J］. Optics and Precision Engineering, 2018, 26(5): 1201-1210. (in Chinese)

[30] [30] JIN Z G, LI J K. Co-saliency detection based on objectness and multi-layer linear model ［J］. Optics and Precision Engineering, 2019, 27(8): 1845-1853. (in Chinese)

[31] [31] LEI J Z, ZENG L B, YE N. Research on industrial robot alignment technique with monocular vision ［J］. Optics and Precision Engineering, 2018, 26(3): 733-741. (in Chinese)

[32] [32] LIN L, WANG X L, YANG W, et al. Discriminatively trained and-or graph models for object shape detection ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(5): 959-972.

[33] [33] ZHENG L, SHEN L Y, TIAN L, et al. Scalable person re-identification: a benchmark ［C］//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Santiago: IEEE, 2015: 1116-1124.

[34] [34] SUN Y F, ZHENG L, DENG W J, et al. SVDNet for pedestrian retrieval ［C］//Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE, 2017: 3820-3828.