A Super Baseline for Pedestrian Re-Identification

In this paper, we propose a new baseline that combines and improves two types of deep learning algorithms to achieve more accurate pedestrian re-identification from images taken by surveillance cameras. We introduce some tricks into a famous strong baseline [1]. The main contributions are: 1) We improve IBN-Net [2] to replace the original ResNet50 (last stride = 1). 2) We perform model learning by using three common databases with different characteristics at the same time, except for the operation part of random erasing augmentation. 3) We add two new classifiers. 4) We optimize the parameters to prevent overfitting. We conducted ablation experiments on each trick using unlearned data and confirmed the effectiveness and stability of the proposed method from the results.