Applying deep learning image enhancement methods to improve person re-identification

Abstract

Person re-identification has gained significant attention in recent years due to its numerous practical applications in video surveillance. However, while artificial intelligence and deep learning methods have enabled substantial progress in particular aspects of this domain, putting together those individual advances to generate practical systems remains a computer vision challenge. Existing methods are typically designed assuming the target person’s images are captured under uniform, stable conditions with similar lighting levels, but this assumption may not hold in real-world scenarios, such as outdoor monitoring over 24 h, as image quality can vary considerably throughout day and night. In this paper, we propose a framework that incorporates image enhancement techniques to improve the performance of a person re-identification model. The proposed approach achieves a significant improvement in a demanding re-identification dataset, raising the mAP from 9.0% using a zero-shot baseline to 65.8% through the combined use of low-light image enhancement methods and noise reduction.