YOLOv8-Powered Intelligent Surveillance: An Integrated Real-Time Framework for Crowd Management, Crime Prevention, and Workplace Safety Monitoring using AI and ML
Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 1493-1500· 0 citations· 17 references
Abstract
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.
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.
M. G, Jaisanth M., B. M· Journal of Ubiquitous Comput...· 0 citations
Rapid urbanization has increased the need for surveillance systems that can monitor multiple public safety risks at the same time. Traditional systems often use separate solutions for facial recognition, vehicle identification, fire detection, and behavioral analysis, resulting in fragmented infrastructure and multiple interfaces for operators to manage. This paper presents City Sentinel, a unified AI-based surveillance framework that integrates six detection capabilities into one scalable platform: facial recognition, automatic number plate recognition (ANPR), fire and smoke detection, weapon and knife detection, violence detection, and road accident detection. The system combines a Next.js operator dashboard, FastAPI backend, cloud-based PostgreSQL event storage, InsightFace and YOLOv8 vision models, and EasyOCR for plate recognition. Camera streams are processed through dedicated inference workers using RTSP. On a workstation equipped with an NVIDIA RTX 3060 GPU, the system achieves a median end-to-end latency of 743 ms and supports four concurrent RTSP streams within a two-second latency limit. It achieves a 91.2% face-match rate, 85.7% plate-reading accuracy, and mAP@0.5 scores of 0.846 to 0.889 across the fire, knife, and weapon detection modules. In user-acceptance testing, operators could enroll a new identity in under one minute and identify a flagged person from live footage in an average of 12 seconds. The results demonstrate that a modular, open-source, multi-model architecture can provide broad surveillance coverage, cloud-based auditability, and flexibility for adding new detection capabilities while maintaining practical real-time performance.
Hanan Syed Shabir, Noor Fatima, Safia Baloch et al.· 0 citations
: This study presents the next generation of an intelligent surveillance system for smart campuses based on vision-language models (VLMs) for real-time multimodal detection of threat. The proposed framework unifies the fight and weapon recognition, face identification and contextual interpretation of events into a unified monitoring pipeline. 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. The system is deployed on edge devices to balance between computational efficiency and real-time performance and then a centralized surveillance dashboard is used to provide actionable insights by consolidating all the alerts and detections in the surveillance system. Preliminary evaluations show high detection accuracy and low latency, which adds to the prospects of using VLM-driven surveillance in educational environments. Beyond the technical validation, the paper discusses ethical challenges, hardware limitations, and pathways for the easy deployment on a scalable basis ultimately aligning with the SDG 16. This research contributes to the development of proactive and autonomous safety mechanisms by integrating the computer vision, language-based reasoning, and edge AI technologies in an integrated surveillance architecture.
M. Kurulekar, Sanjesh Pawale, Tanay Ingale et al.· Proceedings of the 1st Inter...· 0 citations
Traffic accidents remain a crucial problem for intelligent transportation systems (ITS), and the ability to detect them quickly and efficiently is one of the keys to ensuring an appropriate emergency response and improving road safety. Traditional accident monitoring methods often suffer from poor detection accuracy, high computational costs, and reduced robustness when dealing with complex traffic conditions. This paper presents a novel enhanced YOLOv8-based framework for real-time traffic accident detection capable of operating in dynamic road environments.
The proposed framework uses YOLOv8 as the base detection model, which provides powerful feature extraction capabilities, an anchor-free detection mechanism, and a favorable trade-off between accuracy and inference speed. To address the challenges associated with detecting accident-related objects, such as occlusion, illumination variations, and complex traffic backgrounds, the proposed framework incorporates feature augmentation to enhance feature representation. Multi-scale features are fused at different scales to improve local details, thereby enabling the extraction of critical regional features.
Experimental results show that the proposed framework achieves competitive detection performance, with a precision of 94.3%, a recall of 92.7%, and an F1-score of 93.5%, as well as a mAP@0.5 of 96.1%. Furthermore, the proposed framework achieves a real-time inference speed of 58 FPS on the COCO test-dev 2017 dataset, compared with its own smaller YOLOv4-Nano model with an input size of 416 × 416 pixels. Qualitative results further demonstrate the ability of the proposed approach to localize traffic objects in both successful and direct accident scenarios.
In this paper, we propose 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.
Chuwe Ashlet Munashe, Chaoyu Yang· International Journal of Sci...· 0 citations
Crowd monitoring and analysis is a recent initiative that brings public safety as a result (especially if your business is in a crowded area) This work present 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. The system enables the detection of real-time violations in social distancing, entry in restricted areas, and abnormal crowd behavior. In order to tackle these challenges, such as overlapping objects, dense crowds, and dynamic conditions, some domain-specific changes, such as setting anchor boxes manually and adding attention to the split detector networks, have been made. Experiments show that the system is effective and robust in different surveillance situations. When comparing the results of the Modified YOLOv4 with the state-of-the-art models like YOLOv3, Faster R-CNN, SSD, and FairMOT, the Modified YOLOv4 achieved the best precision (96.87%), recall (95.31%), F1-score (96.08%), and accuracy (97.46%) within no time while the real-time video/frame or image processing was performed through deep learning on individual vehicle counting providing better performance of results over these methods [153]. The system runs with an average of 25 FPS; thus, real-time capabilities are guaranteed. In addition to these totals, the performance achieved a 92% detection accuracy, identified situations of social distance violations, Restricted Area Violations, and abnormal motion with highly Reliant Identification. This proves that this intelligent surveillance system operates in very complex environments and in real-time, which makes it suitable for public safety, smart cities, and event management-related applications. The ICA system demonstrates improved performance in crowd analysis and monitoring.
G. Raju, G. N. Kumar· international journal of eng...· 0 citations
With increasing crowd sizes nowadays, the risks of overcrowding, including injuries, accidents, and even fatalities, have become a great concern. Motivated by the need for safer public spaces, this work designed and developed Amneen, a crowd management system that uses Artificial Intelligence (AI) and Computer Vision (CV). The system provides authorities and event organizers with a real-time tool to track crowd density in public places and prevent dangerous situations before they occur. Amneen integrates two AI models: a head detection model using YOLO-11 and an overcrowding prediction model using Stochastic Gradient Descent Regressor (SGDRegressor). Using live video footage from installed cameras, the system estimates the number of people in a specific area, displays crowd statistics through an interactive dashboard, and sends early warnings when the situation worsens. Additionally, by analyzing historical data patterns, the system predicts congestion before it occurs. The detection model demonstrated strong performance in real-time, processing an image in 6.5 ms with a precision of 93.95%, a recall of 90.91%, an F1-score of 92.41%, and a mean Average Precision (mAP) of 96.26%. The prediction model yielded an MAE of 18.11 and an score of 0.53, indicating moderate predictive performance. Unit and usability testing demonstrated effectiveness and ease of use, highlighting its potential to improve the general quality of life.
Rsha Mirza, Dareen Alsulami, Amal Aljadani et al.· Engineering, Technology &...· 0 citations