Random Occlusion-recovery for Person Re-identification

TL;DR

This paper proposes a novel data augmentation method using occlusion and GAN-based de-occlusion to improve person re-identification performance, reducing manual labeling efforts and increasing dataset diversity.

Contribution

It introduces an automatic synthesis of labeled person images via occlusion and GAN de-occlusion, enhancing dataset size and diversity for re-identification models.

Findings

Improved re-identification accuracy on multiple datasets.

Effective augmentation method reduces manual labeling.

GAN-based de-occlusion generates realistic images for training.

Abstract

As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping multiple cameras or across different time with a single camera. Recently, deep learning-based person re-identification models have achieved great success in many benchmarks. However, these supervised models require a large amount of labeled image data, and the process of manual labeling spends much manpower and time. In this study, we introduce a method to automatically synthesize labeled person images and adopt them to increase the sample number per identity for person re-identification datasets. To be specific, we use block rectangles to randomly occlude pedestrian images. Then, a generative adversarial network (GAN) model is proposed to use paired occluded and original images to synthesize the de-occluded images that similar but not identical to the original image. Afterwards, we annotate the de-occluded images with the same labels of their corresponding raw images and use them to augment the number of samples per identity. Finally, we use the augmented datasets to train baseline model. The experiment results on CUHK03, Market-1501 and DukeMTMC-reID datasets show that the effectiveness of the proposed method.