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Person Re-identification by Video Ranking
Taiqing Wang1, Shaogang Gong2, Xiatian Zhu2, and Shengjin Wang1
1Dept. of Electronic Engineering, Tsinghua University, China
2School of EECS, Queen Mary University of London, UK
Abstract. Current person re-identification (re-id) methods typically rely on single-frame imagery features, and ignore space-time information from image sequences. Single-frame (single-shot) visual appearance matching is inherently limited for person re-id in public spaces due to visual ambiguity arising from non-overlapping camera views where viewpoint and lighting changes can cause significant appearance variation. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy image sequences of people where more reliable space-time features can be extracted, whilst simultaneously to learn a video ranking function for person re-id. Also, we introduce a new image sequence re-id dataset (iLIDS-VID) based on the i-LIDS MCT benchmark data. Using the iLIDS-VID and PRID 2011 sequence re-id 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-shot/multi-shot based re-id methods.
LNCS 8692, p. 688 ff.
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© Springer International Publishing Switzerland 2014