Video-based person re-identification (reID) aims to retrieve person videos
with the same identity as a query person across multiple cameras. Spatial and
temporal distractors in person videos, such as background clutter and partial
occlusions over frames, respectively, make this task much more challenging than
image-based person reID. We observe that spatial distractors appear
consistently in a particular location, and temporal distractors show several
patterns, e.g., partial occlusions occur in the first few frames, where such
patterns provide informative cues for predicting which frames to focus on
(i.e., temporal attentions). Based on this, we introduce a novel Spatial and
Temporal Memory Networks (STMN). The spatial memory stores features for spatial
distractors that frequently emerge across video frames, while the temporal
memory saves attentions which are optimized for typical temporal patterns in
person videos. We leverage the spatial and temporal memories to refine
frame-level person representations and to aggregate the refined frame-level
features into a sequence-level person representation, respectively, effectively
handling spatial and temporal distractors in person videos. We also introduce a
memory spread loss preventing our model from addressing particular items only
in the memories. Experimental results on standard benchmarks, including MARS,
DukeMTMC-VideoReID, and LS-VID, demonstrate the effectiveness of our method.

本文提出了一种基于 STMN 的新型人员 reID 方法，通过存储频繁出现在视频帧中的空间干扰和优化人员视频的典型时间模式的注意力，实现了对人员视频中的空间和时间干扰的有效处理。