Jian Lu, Xu Chen, Maoxin Luo, Hangying Wang. Person Re-Identification Research via Deep Learning[J]. Laser & Optoelectronics Progress, 2020, 57(16): 160003
- Laser & Optoelectronics Progress
- Vol. 57, Issue 16, 160003 (2020)
Fig. 1. Person re-identification in video surveillance
Fig. 2. Complete flow chart of person re-identification
Fig. 3. Person's appearance differences. (a) Pose; (b) resolution; (c) lighting; (d) background
Fig. 4. Non-idealized scenes. (a) Occlusion; (b) person misalignment
Fig. 5. Person re-identification verification model
Fig. 6. Person re-identification classification model
Fig. 7. Person re-identification model based on the contrastive loss
Fig. 8. Person re-identification model based on triplet loss
Fig. 9. Method based on pose estimation models
Fig. 10. AlignedReID[58] calculates the local distance by finding the shortest path
Fig. 11. Difficulties in partial person re-identificaiton. (a) Spatial misalignment; (b) noise from unshared regions
Fig. 12. Unconditional generative model
Fig. 13. Pose-conditioned generative model
Fig. 14. Intra-domain and inter-domain image styles differences
Fig. 15. Accuracy of 14 typical methods in the Market1501
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Table 1. Person re-identification common datasets
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Table 2. Partial person re-identification common datasets
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