Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification

TL;DR

This paper introduces SeqMasks, a framework combining appearance and gait features for video-based person re-identification, addressing challenges like view variation and scene noise, validated on new and existing datasets.

Contribution

The paper proposes SeqMasks, a novel approach that integrates appearance and gait modeling for improved person re-ID, supported by a new dataset and extensive experiments.

Findings

Superior performance on MaskMARS dataset

Effective gait and appearance feature integration

Good generalization across different datasets

Abstract

ideo-based person re-identification (Re-ID) aims to match person images in video sequences captured by disjoint surveillance cameras. Traditional video-based person Re-ID methods focus on exploring appearance information, thus, vulnerable against illumination changes, scene noises, camera parameters, and especially clothes/carrying variations. Gait recognition provides an implicit biometric solution to alleviate the above headache. Nonetheless, it experiences severe performance degeneration as camera view varies. In an attempt to address these problems, in this paper, we propose a framework that utilizes the sequence masks (SeqMasks) in the video to integrate appearance information and gait modeling in a close fashion. Specifically, to sufficiently validate the effectiveness of our method, we build a novel dataset named MaskMARS based on MARS. Comprehensive experiments on our proposed large wild video Re-ID dataset MaskMARS evidenced our extraordinary performance and generalization capability. Validations on the gait recognition metric CASIA-B dataset further demonstrated the capability of our hybrid model.