An Improved Person Re-identification Method by light-weight convolutional neural network

TL;DR

This paper presents a lightweight Siamese network using EfficientNet and verification loss to improve person re-identification accuracy, especially in low-resource settings, outperforming state-of-the-art methods on CUHK01 dataset.

Contribution

The paper introduces a lightweight, transfer learning-based Siamese network with verification loss for person re-identification, enhancing accuracy and efficiency.

Findings

Achieved 95.2% rank-5 accuracy on CUHK01 dataset.

Reduced model size leads to faster learning and less hardware requirement.

Outperformed existing methods in re-identification accuracy.

Abstract

Person Re-identification is defined as a recognizing process where the person is observed by non-overlapping cameras at different places. In the last decade, the rise in the applications and importance of Person Re-identification for surveillance systems popularized this subject in different areas of computer vision. Person Re-identification is faced with challenges such as low resolution, varying poses, illumination, background clutter, and occlusion, which could affect the result of recognizing process. The present paper aims to improve Person Re-identification using transfer learning and application of verification loss function within the framework of Siamese network. The Siamese network receives image pairs as inputs and extract their features via a pre-trained model. EfficientNet was employed to obtain discriminative features and reduce the demands for data. The advantages of verification loss were used in the network learning. Experiments showed that the proposed model performs better than state-of-the-art methods on the CUHK01 dataset. For example, rank5 accuracies are 95.2% (+5.7) for the CUHK01 datasets. It also achieved an acceptable percentage in Rank 1. Because of the small size of the pre-trained model parameters, learning speeds up and there will be a need for less hardware and data.