Jul 2026· Journal of Ubiquitous Computing and Communication Technologies· Vol 8, pp. 214-232· 0 citations· 10 references
TL;DR
A hybrid intelligent crowd monitoring system comprising YOLOv8-based person detection, crowd density estimation, crowd flow analysis, abnormal crowd behavior detection, and alert generation is introduced.
Abstract
Crowd monitoring has become an essential component of public safety management through continuous monitoring and detection of potential risks in congested areas. In this research, a hybrid intelligent crowd monitoring system comprising YOLOv8-based person detection, crowd density estimation, crowd flow analysis, abnormal crowd behavior detection, and alert generation is introduced. The design and implementation of the proposed hybrid intelligent crowd monitoring system have been accomplished utilizing the DMADV methodology in order to set up a defined design and implementation process. First of all, video frames from the surveillance cameras undergo pre-processing in order to improve their quality and then analyzed using YOLOv8 algorithm for person detection. Finally, the detected persons will be used for the estimation of crowd density and flow analysis as well as abnormal crowd behavior detection in real time. If the predetermined safety thresholds are exceeded, the automatic alert is generated.
The evolving complexity of urban environments and the effectiveness of traditional CCTV surveillance is making it increasingly difficult to ensure public safety, effective crowd management, crime prevention, and workplace security solutions. However, the traditional approach to surveillance is largely manual, leading to late reactions, missed events, and scalability issues. The intelligent surveillance system based on the YOLOv8 object detection algorithm is designed to enhance workplace safety, prevent crimes, manage crowds, and achieve face recognition in real time within a single AI platform. This paper introduces the concept of an intelligent surveillance framework that combines real-time face recognition, workplace safety monitoring, crowd management, and crime prevention through the use of YOLOv8 object detection algorithms within a single AI-driven solution. The framework employs the YOLOv8 algorithm for object detection, identifying people, weapons, suspicious activities, abandoned objects, and workplace safety violations, and issuing automatic alerts to facilitate swift decision-making. The proposed framework provides an integrated platform of multiple surveillance functionalities as opposed to the existing surveillance systems, where each surveillance task is monitored separately, which provides overall situational awareness using the existing CCTV. The model was trained with surveillance images annotated and tested with Precision, Recall, F1-score, Accuracy, and mAP@0.5. An overall detection accuracy of 92.4%, a precision of 92.4%, a recall of 89.7%, an F1-score of 91.0%, and an mAP@0.5 of 93.2% have been achieved during experimental evaluation. Moreover, the framework's average inference latency is 18ms per frame, which guarantees that it can be used in real-time surveillance applications without compromising the accuracy of its detection results when deployed in various surveillance environments. The proposed system is versatile and feasible for implementation in smart city systems, transportation hubs, industrial production sites, and various organizational environments, and can enable smart surveillance by merging multiple security functions into a single framework based on the YOLOv8 object detection model.
M. Anusha, N. Prashanth, T. Swetha et al.· 2026 International Conferenc...· 0 citations
This work presents a novel Intelligent Crowd Analysis (ICA) system in this paper that takes advantage of a Modified YOLOv4-tiny object detection model with Non-Maximum Suppression (NMS), Deep SORT object tracking and a Crowd Monitoring and Behavior Analysis (CMBA)module that is designed by us.
G. Raju, G. N. Kumar· international journal of eng...· 0 citations
Railway stations, religious events, and other large public gatherings are prone to overcrowding, sometimes resulting in stampede situations. The existing systems for monitoring crowds primarily rely on manual monitoring, which prevents them from producing early warnings for potentially dangerous situations. In this paper, the authors describe a machine vision-enabled crowd monitoring and early warning framework that is based on YOLOv8m, CNN, and LSTM models with the ability to perform real-time analysis of crowds for the purpose of predicting the risk of a stampede. The framework uses real-time video streams from surveillance cameras for the processes of crowd detection, density estimation, motion analysis, flow direction tracking, and prediction of temporal behaviour. Results demonstrate that the proposed framework successfully identifies abnormal crowd behaviour and predicts the potential for extreme or dangerous situations prior to their occurring. The proposed system enhances overall crowd safety within a high-density area, enhances the accuracy of detecting abnormal behaviour, and improves the ability to provide early warning capability of a potentially dangerous crowd situation.
Priyanka Parashar, Poonam Bhartiya, Shailendra Shriwastava· International Journal of Com...· 0 citations
Amneen is a crowd management system that uses Artificial Intelligence (AI) and Computer Vision (CV) to track crowd density in public places and prevent dangerous situations before they occur, and predicts congestion before it occurs.
Rsha Mirza, Dareen Alsulami, Amal Aljadani et al.· Engineering, Technology &...· 0 citations
An effective real-time traffic accident detection framework based on YOLOv8 that can be implemented in intelligent transportation systems, traffic surveillance platforms, and advanced driver assistance applications is proposed.
Chuwe Ashlet Munashe, Chaoyu Yang· International Journal of Sci...· 0 citations
The proposed framework unifies the fight and weapon recognition, face identification and contextual interpretation of events into a unified monitoring pipeline, and a novel contribution of this work is the tool calling that allows the VLM to automatically seize important frames and trigger alert protocols in such a way that it will reduce man-in, and response delays.
M. Kurulekar, Sanjesh Pawale, Tanay Ingale et al.· Proceedings of the 1st Inter...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
