Video-based person re-identification is a crucial task of matching video
sequences of a person across multiple camera views. Generally, features
directly extracted from a single frame suffer from occlusion, blur,
illumination and posture changes. This leads to false activation or missing
activation in some regions, which corrupts the appearance and motion
representation. How to explore the abundant spatial-temporal information in
video sequences is the key to solve this problem. To this end, we propose a
Refining Recurrent Unit (RRU) that recovers the missing parts and suppresses
noisy parts of the current frame's features by referring historical frames.
With RRU, the quality of each frame's appearance representation is improved.
Then we use the Spatial-Temporal clues Integration Module (STIM) to mine the
spatial-temporal information from those upgraded features. Meanwhile, the
multi-level training objective is used to enhance the capability of RRU and
STIM. Through the cooperation of those modules, the spatial and temporal
features mutually promote each other and the final spatial-temporal feature
representation is more discriminative and robust. Extensive experiments are
conducted on three challenging datasets, i.e., iLIDS-VID, PRID-2011 and MARS.
The experimental results demonstrate that our approach outperforms existing
state-of-the-art methods of video-based person re-identification on iLIDS-VID
and MARS and achieves favorable results on PRID-2011.

本文提出一种基于视频序列的人物再识别方法，通过 Refining Recurrent Unit 和 Spatial-Temporal clues Integration Module 来更好地表达视频序列中的空间和时间信息，并且采用了多层次训练目标来增强算法的性能。实验结果表明，该方法在 iLIDS-VID 和 MARS 数据集上优于现有的最先进方法，并在 PRID-2011 上取得了良好的结果。