Improved Algorithm for Person Re-Identification Based on Global Features

Tao Zhang; Zhengming Yi; Xuan Li; Xing Sun

doi:10.3788/LOP57.241503

[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] Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 15523970(2015).

[3] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[4] Huang G. Liu Z, van der Maaten L, et al. Densely connected convolutional networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).

[5] Bi X J, Wang H. Person re-identification based on view information embedding[J]. Acta Optica Sinica, 39, 0615007(2019).

[6] Pan T, Li W G. Person re-identification based on feature stitching[J]. Laser & Optoelectronics Progress, 56, 162001(2019).

[7] Xu L Z, Peng L. Person reidentification based on multiscale convolutional feature fusion[J]. Laser & Optoelectronics Progress, 56, 141504(2019).

[8] Hermans A, Beyer L. -11-21)[2020-05-25]. https:∥arxiv., org/abs/1703, 07737(2017).

[9] Schroff F, Kalenichenko D, Philbin J. FaceNet: a unified embedding for face recognition and clustering. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 815-823(2015).

[10] Luo H, Jiang W, Gu Y Z et al. A strong baseline and batch normalization neck for deep person re-identification[J]. IEEE Transactions on Multimedia, 22, 2597-2609(2020).

[11] Zheng L, Shen L Y, Tian L et al. Scalable person re-identification: a benchmark. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1116-1124(2015).

[12] 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. Lecture notes in computer science. Cham: Springer, 9914, 17-35(2016).

[13] 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, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 152-159(2014).

[14] Zhong Z, Zheng L, Cao D L et al. Re-ranking person re-identification with k-reciprocal encoding. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 3652-3661(2017).

[15] Luo H, Jiang W, Zhang X et al. AlignedReID++: dynamically matching local information for person re-identification[J]. Pattern Recognition, 94, 53-61(2019).

[16] Fan X, Luo H, Zhang X et al. SCPNet: spatial-channel parallelism network for joint holistic and partial person re-identification[M]. ∥Jawahar C, Li H, Mori G, et al. Computer vision-ACCV 2018. Lecture notes in computer science. Cham: Springer, 11362, 19-34(2019).

[17] 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. Cham: Springer, 11208, 501-518(2018).

[18] Zheng F, Deng C, Sun X et al. Pyramidal person re-identification via multi-loss dynamic training. [C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 8506-8514(2019).

[19] Fu Y, Wei Y, Zhou Y et al. Horizontal pyramid matching for person reidentification. [C]∥The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), January 27-February 1, 2019, Honolulu, Hawaii, USA. California: AAAI, 8295-8302(2019).

[20] Wang G S, Yuan Y F, Chen X et al. Learning discriminative features with multiple granularities for person re-identification. [C]∥2018 ACM Multimedia Conference on Multimedia Conference-MM'18, October 15-26, 2018. Seoul, Republic of Korea. New York: ACM, 274-282(2018).

[21] 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, 13(2017).

[22] Sun Y F, Zheng L, Deng W J et al. SVDNet for pedestrian retrieval. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 3820-3828(2017).

[23] Ristani E, Tomasi C. Features for multi-target multi-camera tracking and reidentification. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 6036-6046(2018).

[24] Chang X B, Hospedales T M, Xiang T. Multi-level factorisation net for person re-identification. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2109-2118(2018).

[25] Li W, Zhu X, Gong S. Harmonious attention network for person re-identification. [C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 18-22, 2018, Salt Lake City, UT, USA. New York: IEEE, 2285-2294(2018).

[26] 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, 29, 3037-3045(2019).