# Advancements In Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-The-Art

arXiv: https://arxiv.org/abs/2308.03907 · HTML: https://mlsanity.com/paper/2308.03907/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 96 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing](https://mlsanity.com/paper/1904.06400/) (2019)
- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects](https://mlsanity.com/paper/2202.03274/) (2022)

## Built on by (most cited first, 1 of 1)

- [Integrating Generative Adversarial Networks and Convolutional Neural Networks for Enhanced Traffic Accidents Detection and Analysis](https://mlsanity.com/paper/2506.16186/) (2025)

## Abstract (arXiv)

Growing apprehensions surrounding public safety have captured the attention of numerous governments and security agencies across the globe. These entities are increasingly acknowledging the imperative need for reliable and secure crowd-monitoring systems to address these concerns. Effectively managing human gatherings necessitates proactive measures to prevent unforeseen events or complications, ensuring a safe and well-coordinated environment. The scarcity of research focusing on crowd monitoring systems and their security implications has given rise to a burgeoning area of investigation, exploring potential approaches to safeguard human congregations effectively. Crowd monitoring systems depend on a bifurcated approach, encompassing vision-based and non-vision-based technologies. An in-depth analysis of these two methodologies will be conducted in this research. The efficacy of these approaches is contingent upon the specific environment and temporal context in which they are deployed, as they each offer distinct advantages. This paper endeavors to present an in-depth analysis of the recent incorporation of artificial intelligence (AI) algorithms and models into automated systems, emphasizing their contemporary applications and effectiveness in various contexts.