Estimating crowd density is a cornerstone of modern urban management and public safety, particularly in the aftermath of catastrophic incidents, such as the 2015 Mina stampede. With the rapid advancement of artificial intelligence (AI) technologies, deep learning (DL) has emerged as a powerful tool for addressing these challenges. This systematic review provides a comprehensive evaluation of current crowd density estimation methodologies, analyzing model architectures, datasets, and research trends. The review was conducted in accordance with PRISMA 2020 guidelines, and the search encompassed five major electronic databases (IEEE Xplore, Scopus, Google Scholar, Web of Science, and ScienceDirect) for the period 2020 to 2025. The selection process relied on rigorous eligibility criteria, including English-language publications that offer methodological contributions or empirical assessments in the field of computer vision and machine learning (ML). Twenty final studies were included, 70% of which were published in scientific journals. The analysis revealed that 55% of the studies relied entirely on DL models, while 30% leaned towards hybrid modelling. The ShanghaiTech dataset remained the most frequently used benchmark, accounting for 50% of the studies, followed by UCF CC 50 and WorldExpo’10 datasets. Although some models achieved a high accuracy of 99.88%, they still faced challenges in highly congested scenes and visual obstructions. This review reveals a growing shift towards edge intelligence and lightweight models to reduce latency, with a pressing need for more diverse datasets to minimize bias. This study concludes that bridging the gap between simulation and reality requires integrating contextual information and behavioral analysis to enable more reliable, proactive, and real-time crowd management.
Aloufi et al. (2026) studied this question.