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.

本文提出了全局 - 局部时间表征（GLTR）的方法，以利用视频序列中的多尺度时间线索进行视频人物重新识别。这种方法通过对相邻帧中的短时时间线索进行建模，然后捕捉不连续帧之间的长期关系来构建。其中，短期时间线索是通过不同的时间扩张率平行膨胀卷积来表示行人的运动和外貌。利用时间自我注意模型来捕捉长期关系，以减轻视频序列中的遮挡和噪音。最终，通过简单的单流卷积神经网络将短期和长期时间线索聚合形成 GLTR。在四个广泛使用的视频 ReID 数据集上，GLTR 相对于基于身体部分线索或度量学习学习到的现有特征表现出显著的优越性。例如，在 MARS 数据集上，它取得了 87.02％的 Rank-1 精度，而且没有经过一次排序，优于目前的最新技术水平。