In this work, we survey the state of the art of person re-identification and introduce the basics of the deep learning method for implementing this task. Moreover, we propose a new structure for this task.
The core content of our work is to optimize the model that is composed of a pre-trained network to distinguish images from different people with representative features. The experiment is implemented on three public person datasets and evaluated with evaluation metrics that are mean Average Precision (mAP) and Cumulative Matching Characteristic (CMC).
We take the BNNeck structure proposed by Luo et al. [25] as the baseline model. It adopts several tricks for the training, such as the mini-batch strategy of loading images, data augmentation for improving the model’s robustness, dynamic learning rate, label-smoothing regularization, and the L2 regularization to reach a remarkable performance. Inspired from that, we propose a novel structure named SplitReID that trains the model in separated feature embedding spaces with multiple losses, which outperforms the BNNeck structure and achieves competitive performance on three datasets. Additionally, the SplitReID structure holds the property of lightweight computation complexity that it requires fewer parameters for the training and inference compared to the BNNeck structure.
Person re-identification can be deployed without high-resolution images and fixed angle of pedestrians with the deep learning method to achieve outstanding performance. Therefore, it holds an immeasurable prospect in practical applications, especially for the security fields, even though there are still some challenges like occlusions to be overcome
