Abstract: Disclosed is a video personnel re-identification method based on trajectory fusion in a complex underground space; an accurate personnel trajectory prediction may be realized through the Social-GAN model; and a spatio-temporal trajectory fusion model is constructed, and personnel trajectory videos that are not affected by the occlusion are introduced into the re-identification network to solve the problem of false extraction of the apparent visual features caused by the occlusion. In addition, a trajectory fusion MARS_traj data set is constructed, and a number of time frames and space coordinate information are added to the MARS data set.