  Video person re-identification (re-ID) plays an important role in
surveillance video analysis. However, the performance of video re-ID
degenerates severely under partial occlusion. In this paper, we propose a novel
network, called Spatio-Temporal Completion network (STCnet), to explicitly
handle partial occlusion problem. Different from most previous works that
discard the occluded frames, STCnet can recover the appearance of the occluded
parts. For one thing, the spatial structure of a pedestrian frame can be used
to predict the occluded body parts from the unoccluded body parts of this
frame. For another, the temporal patterns of pedestrian sequence provide
important clues to generate the contents of occluded parts. With the
Spatio-temporal information, STCnet can recover the appearance for the occluded
parts, which could be leveraged with those unoccluded parts for more accurate
video re-ID. By combining a re-ID network with STCnet, a video re-ID framework
robust to partial occlusion (VRSTC) is proposed. Experiments on three
challenging video re-ID databases demonstrate that the proposed approach
outperforms the state-of-the-art.
