Person re-identification for smart cities: State-of-the-art and the path ahead

Abstract

Abstract One of the indispensable pillars of a smart city is its surveillance infrastructure, and it requires smart techniques to analyze the videos acquired from the surveillance cameras. Person re-identification (PRId) is one of the fundamental tasks in automated visual surveillance, and it has been an area of extensive research spanning the past decade. PRId aims at finding a person who has previously been seen or identified using some unique descriptor of the person. This survey comprises a broad spectrum of PRId methods spanning from traditional to deep-learning, being analyzed and compared. This survey also discusses various PRId frameworks based on machine learning and deep learning. This study emphasizes the challenges in building PRId systems for the benefits of smart cities and presents a critical overview of recent progress and the state-of-the-art approaches to solving some significant challenges of existing PRId systems.