Cross-View Kernel Similarity Metric Learning Using Pairwise Constraints for Person Re-identification

TL;DR

This paper introduces a non-linear kernel-based similarity metric learning method for person re-identification that effectively handles small training datasets and complex appearance variations across camera views.

Contribution

It extends XQDA to a non-linear kernel framework, improving handling of complex non-linearities in person re-ID with computational efficiency.

Findings

Achieves competitive performance on four challenging datasets.

Handles small training data effectively.

Maintains computational efficiency.

Abstract

Person re-identification is the task of matching pedestrian images across non-overlapping cameras. In this paper, we propose a non-linear cross-view similarity metric learning for handling small size training data in practical re-ID systems. The method employs non-linear mappings combined with cross-view discriminative subspace learning and cross-view distance metric learning based on pairwise similarity constraints. It is a natural extension of XQDA from linear to non-linear mappings using kernels, and learns non-linear transformations for efficiently handling complex non-linearity of person appearance across camera views. Importantly, the proposed method is very computationally efficient. Extensive experiments on four challenging datasets shows that our method attains competitive performance against state-of-the-art methods.