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IEEE Trans Pattern Anal Mach Intell ; 38(12): 2501-2514, 2016 12.
Article in English | MEDLINE | ID: mdl-26829777

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.


Subject(s)
Algorithms , Biometric Identification/methods , Discriminant Analysis , Image Interpretation, Computer-Assisted/methods , Pattern Recognition, Automated/methods , Photography/methods , Video Recording/methods , Humans
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