Person re-identification: A retrospective on domain specific open challenges and future trends

Abstract

Person Re-Identification (Re-ID) is a critical aspect of visual surveillance systems, which aims to automatically recognize and locate individuals across a multi-camera network with non-overlapping fields-of-view. Despite significant progress in recent years through the use of deep learning-based approaches, there remain many vision-related challenges, such as occlusion, pose, background clutter, misalignment, scale, viewpoint, low resolution & illumination, and cross-domain generalization across camera modalities, that hinder the accurate identification of individuals. The majority of the proposed approaches directly or indirectly aim to solve one or multiple of these existing challenges. To further advance the development of Re-ID solutions, a comprehensive review of the current approaches is necessary. However, no focused review currently exists that analyses and highlights specific aspects for further development. To fill this gap, we present a systematic challenge-specific literature survey of about 300 papers published between 2015 and 2022, which reviews Re-ID approaches from a solution-oriented perspective. This survey is the first of its kind to provide an in-depth analysis of the different approaches used to address the various challenges in Re-ID. Furthermore, our review highlights several prominent and diverse research trends in the Re-ID domain. These trends offer a visionary perspective regarding ongoing person Re-ID research, and they may eventually lead to the development of practical real-world solutions. We highlighted the AI ethics that must be followed while developing a Re-ID solution, and recently being practiced as well. Another exciting future dimension of person Re-ID research is the long-term Re-ID, which is still under evolution. Overall, our survey aims to serve as a valuable resource for researchers and practitioners working in the field of Re-ID and to inspire the development of innovative and effective Re-ID solutions.