Advancing Person Re-Identification: Tensor-based Feature Fusion and Multilinear Subspace Learning
CoRR(2023)
摘要
Person re-identification (PRe-ID) is a computer vision issue, that has been a
fertile research area in the last few years. It aims to identify persons across
different non-overlapping camera views. In this paper, We propose a novel
PRe-ID system that combines tensor feature representation and multilinear
subspace learning. Our method exploits the power of pre-trained Convolutional
Neural Networks (CNNs) as a strong deep feature extractor, along with two
complementary descriptors, Local Maximal Occurrence (LOMO) and Gaussian Of
Gaussian (GOG). Then, Tensor-based Cross-View Quadratic Discriminant Analysis
(TXQDA) is used to learn a discriminative subspace that enhances the
separability between different individuals. Mahalanobis distance is used to
match and similarity computation between query and gallery samples. Finally, we
evaluate our approach by conducting experiments on three datasets VIPeR, GRID,
and PRID450s.
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关键词
Person Re-Identification,Multilinear subspace learning,Tensor feature fusion,pre-trained CNN,LOMO,GOG,TXQDA
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