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ideo-based person re-identification (Re-ID) aims to match person images invideo sequences captured by disjoint surveillance cameras. Traditionalvideo-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 animplicit biometric solution to alleviate the above headache. Nonetheless, itexperiences severe performance degeneration as camera view varies. In anattempt to address these problems, in this paper, we propose a framework thatutilizes the sequence masks (SeqMasks) in the video to integrate appearanceinformation and gait modeling in a close fashion. Specifically, to sufficientlyvalidate the effectiveness of our method, we build a novel dataset namedMaskMARS based on MARS. Comprehensive experiments on our proposed large wildvideo Re-ID dataset MaskMARS evidenced our extraordinary performance andgeneralization capability. Validations on the gait recognition metric CASIA-Bdataset further demonstrated the capability of our hybrid model.

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