Temporal Reordering for Video Person Re-identification Based on Feature Reappearance Score

Jing Wang, Bingpeng Ma

Abstract: This paper proposes a novel model, named Temporal Reordering Vision Transformer (TRViT) based on feature reappearance score for video person re-identification. The current methods do not assess whether redundancy or interference is present in the video, and treats all the videos in a same way, which only handles one issue but ignores the other. To address the problem, this paper proposed a novel metric, Feature Reappearance Score (FRS), to quantitively evaluate the redundancy degree of sample videos, and determine the sample should be treated in a redundancy-focused measure or disturbance-focused measure. Further, in order to provide a unified solution to both issues, we propose the temporal reordering method. Through reordering the sequence according to different criteria, we can emphasize the extraction of distinctive or common features in the video. Our method is evaluated on two widely used and challenging datasets. The experimental results show that it outperforms the state-of-the-art methods.

External IDs:dblp:conf/icig/WangM25