title: Person Re-Identification by Discriminative Selection in Video Ranking.
abstract: Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios. Single-frame (single-shot) based visual appearance matching is inherently limited for person ReID in public spaces due to the challenging visual ambiguity and uncertainty arising from non-overlapping camera views where viewing condition changes can cause significant people appearance variations. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy/incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID. Using the PRID 2011, iLIDS-VID, and HDA+ image sequence datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art single-/multi-shot ReID methods.
authors.forename: Taiqing
authors.initials: T
authors.lastname: Wang
authors.forename: Shaogang
authors.initials: S
authors.lastname: Gong
authors.forename: Xiatian
authors.initials: X
authors.lastname: Zhu
authors.forename: Shengjin
authors.initials: S
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016002:Discriminant Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D014741:Video Recording / Q000379:methods*