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Recently, video-based person re-identification (re-ID) has drawn increasingattention in compute vision community because of its practical applicationprospects. Due to the inaccurate person detections and pose changes, pedestrianmisalignment significantly increases the difficulty of feature extraction andmatching. 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, apose-based reference feature learning module is introduced. Secondly, aneffective relation-based part feature disentangling module is explored to alignframes within each video. By means of using both modules, the informative partsof pedestrian in videos are well aligned and more discriminative featurerepresentation is generated. Comprehensive experiments on three widely-usedbenchmarks, i.e. iLIDS-VID, PRID-2011 and MARS datasets verify theeffectiveness of the proposed framework. Our code will be made publiclyavailable.

Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification