  This paper proposes the Global-Local Temporal Representation (GLTR) to
exploit the multi-scale temporal cues in video sequences for video person
Re-Identification (ReID). GLTR is constructed by first modeling the short-term
temporal cues among adjacent frames, then capturing the long-term relations
among inconsecutive frames. Specifically, the short-term temporal cues are
modeled by parallel dilated convolutions with different temporal dilation rates
to represent the motion and appearance of pedestrian. The long-term relations
are captured by a temporal self-attention model to alleviate the occlusions and
noises in video sequences. The short and long-term temporal cues are aggregated
as the final GLTR by a simple single-stream CNN. GLTR shows substantial
superiority to existing features learned with body part cues or metric learning
on four widely-used video ReID datasets. For instance, it achieves Rank-1
Accuracy of 87.02% on MARS dataset without re-ranking, better than current
state-of-the art.
