Input： MARS_traj dataset；trajectory prediction model Social- GAN；video-based person re-identification model

Output： mAP and Rank-k

1）spatial coordinates and temporal information of person ID from query dataset of video sequences are input into Social-GAN model；

2）possible predicted trajectories are generated by generator in Social-GAN based on spatial coordinates and temporal information；

3）discriminator in Social-GAN discriminates generated predicted trajectory，and obtains matching predicted trajectory dataset query_pred.

4）For $i=1:N_1$ do

5）  For $j=1:N_2$ do

6）   temporal fusion loss $l_j^{\mathrm{t}\mathrm{e}\mathrm{m}}$ and spatial fusion loss $l_j^{\mathrm{s}\mathrm{a}\mathrm{p}}$ between $j$-th video sequence in gallery and $i$-th video prediction trajectory in query_pred are computed by equation（3）and（4），respectively；

7）  end

8）  $l_{i-j}=\mathrm{m}\mathrm{i}\mathrm{n}\left(l_j^{\mathrm{t}\mathrm{e}\mathrm{m}}+l_j^{\mathrm{s}\mathrm{a}\mathrm{p}}\right);\forall j\in \left[1,N_2\right];$

9）  value of $j$ corresponding to $l_{i-j}$ is obtained and assigned to $i_j$；

10）  sent $i_j$-th video sequence of gallery into query_TP；

11）end

12）fusion feature of query_TP and gallery are extracted，respectively；

13）feature distance metrics based on query_TP and gallery are calculated，and feature vectors of all gallery video sequences are ranked according to distance metrics；

14）probability of correct match within ranked gallery is calculated according to query；

15）return mAP and Rank-k.