Cross-View Kernel Similarity Metric Learning Using Pairwise Constraints for Person Re-identification
This work addresses the problem of matching pedestrians across cameras for surveillance applications, but it is incremental as it extends an existing linear method to non-linear mappings.
The paper tackles person re-identification by proposing a non-linear cross-view similarity metric learning method to handle small training data, achieving competitive performance on four challenging datasets.
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.