  Recently, video-based person re-identification (re-ID) has drawn increasing
attention in compute vision community because of its practical application
prospects. Due to the inaccurate person detections and pose changes, pedestrian
misalignment significantly increases the difficulty of feature extraction and
matching. To address this problem, in this paper, we propose a
\textbf{R}eference-\textbf{A}ided \textbf{P}art-\textbf{A}ligned
(\textbf{RAPA}) framework to disentangle robust features of different parts.
Firstly, in order to obtain better references between different videos, a
pose-based reference feature learning module is introduced. Secondly, an
effective relation-based part feature disentangling module is explored to align
frames within each video. By means of using both modules, the informative parts
of pedestrian in videos are well aligned and more discriminative feature
representation is generated. Comprehensive experiments on three widely-used
benchmarks, i.e. iLIDS-VID, PRID-2011 and MARS datasets verify the
effectiveness of the proposed framework. Our code will be made publicly
available.
