Detection and Tracking of People in a Dense Crowd through Deep Learning Approach-A Systematic Literature Review

Crowd-related incidents, such as the Hillsborough Disaster and the Kanjuruhan Stadium stampede, often result from poor crowd management, leading to tragedies like suffocation and crushing. To mitigate human error in crowd control, this research explores the use of deep learning for the detection and tracking of individuals in dense crowds. The study focuses on implementing artificial intelligence for automated crowd monitoring through a localization map, with an emphasis on re-identification accuracy and auto-annotation of targets in datasets. A Systematic Literature Review (SLR) was conducted following the PRISMA guidelines, analyzing 4384 articles published between 2019 and 2024 across five databases. 13 primary studies met the inclusion criteria and were analyzed to address questions related to the accuracy of crowd tracking and detection. This SLR aims to provide insights and reference points for further research in artificial intelligence, particularly in the areas of auto annotation and re-identification for crowd monitoring.