Person Reidentification via Multi-Feature Fusion with Adaptive Graph Learning

Runwu Zhou, Xiaojun Chang, Lei Shi, Yi Dong Shen, Yi Yang, Feiping Nie

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160 引用 (Scopus)

摘要

The goal of person reidentification (Re-ID) is to identify a given pedestrian from a network of nonoverlapping surveillance cameras. Most existing works follow the supervised learning paradigm which requires pairwise labeled training data for each pair of cameras. However, this limits their scalability to real-world applications where abundant unlabeled data are available. To address this issue, we propose a multi-feature fusion with adaptive graph learning model for unsupervised Re-ID. Our model aims to negotiate comprehensive assessment on the consistent graph structure of pedestrians with the help of special information of feature descriptors. Specifically, we incorporate multi-feature dictionary learning and adaptive multi-feature graph learning into a unified learning model such that the learned dictionaries are discriminative and the subsequent graph structure learning is accurate. An alternating optimization algorithm with proved convergence is developed to solve the final optimization objective. Extensive experiments on four benchmark data sets demonstrate the superiority and effectiveness of the proposed method.

源语言英语
文章编号8755330
页(从-至)1592-1601
页数10
期刊IEEE Transactions on Neural Networks and Learning Systems
31
5
DOI
出版状态已出版 - 5月 2020

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