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arXiv · 1601.06260

Person Re-Identification by Discriminative Selection in Video Ranking

Taiqing Wang·Shaogang Gong·Xiatian Zhu·Shengjin Wang
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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.

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Taiqing Wang, Shaogang Gong, Xiatian Zhu, Shengjin Wang. 2016-01-23. Person Re-Identification by Discriminative Selection in Video Ranking. https://arxiv.org/abs/1601.06260

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