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