A hybrid multilinear-linear subspace learning approach for enhanced person re-identification in camera networks

Abstract

Several tasks in surveillance systems depend on camera networks, one of them is Person re-identification (PRe-ID) which has wide interest as a research topic in the computer vision field. The current techniques in this issue encounter many obstacles in handling the variability of appearance, extracting effective features, and capturing intricate data relationships, leading to suboptimal performance causing systems to have low performance. This paper presents a novel hybrid subspace learning approach for Person re-identification (PRe-ID), that leverages the strengths of both multilinear and linear techniques to significantly enhance the performance of PRe-ID. The proposed process begins by extracting distinctive effective features from pedestrian images using LOMO and GOG as shallow feature descriptors with CNN for deep features, followed by the application of two stages of subspace learning. In the first stage, we employ a multilinear subspace learning algorithm known as Tensor-based Quadratic Discriminant Analysis (TXQDA). TXQDA captures complex relationships and interactions among different features, resulting in a more discriminative feature representation. Subsequently, we employ a linear subspace learning technique called Side Information Linear Discriminant Analysis (SILD) to refine the subspace learned from TXQDA. SILD utilizes linear projections to enhance discriminability and preserve crucial inter-class variations, thereby improving the overall performance of PRe-ID. Ultimately, we implement logistic regression (LR) fusion at the score level to enhance the performance of our hybrid subspace learning approach, particularly within various tensor feature representation scenarios. Extensive experiments are conducted on three challenging datasets to evaluate the effectiveness of our hybrid subspace learning approach. The experimental results demonstrate that our method achieves superior performance compared to state-of-the-art techniques in the field of PRe-ID.