Intelligent Surveillance Using YOLO-Based Object Detection and Automated Mobile Alert System

Carlo Miguel Garcia; Kevin Angelo Torres

doi:https://doi.org/10.47001/CJESR/2026.306006

Authors

Carlo Miguel Garcia

Department of Information Systems and Computer Science, Ateneo de Manila, Philippines

Kevin Angelo Torres

Department of Information Systems and Computer Science, Ateneo de Manila, Philippines

Abstract

The rapid advancement of computer vision and deep learning technologies has significantly improved intelligent surveillance systems by enabling accurate real-time object detection and automated threat monitoring. Conventional surveillance systems primarily rely on continuous human observation, which is often inefficient, time-consuming, and prone to delayed responses during critical situations. To address these limitations, this paper proposes an Intelligent Surveillance Using YOLO-Based Object Detection and Automated Mobile Alert System, a smart monitoring framework that integrates state-of-the-art object detection algorithms with real-time mobile notification services for enhanced public safety and security. The proposed system utilizes the You Only Look Once (YOLO) deep learning architecture to detect and classify multiple objects, including pedestrians, vehicles, bicycles, motorcycles, and other predefined categories from both static images and live video streams. Specifically, YOLOv3 is employed for image-based object detection due to its robust detection capability, while YOLOv8 is adopted for real-time video surveillance because of its improved speed, efficiency, and detection accuracy. Following object detection, the system estimates essential parameters such as object location, distance, speed, and movement direction to assess potential safety risks. A rule-based risk assessment module continuously analyzes these parameters and determines whether an object poses a potential threat based on predefined safety thresholds. When a high-risk event is detected, the system automatically generates an alert and transmits an SMS notification to authorized users through the Twilio cloud communication platform, enabling immediate awareness and rapid response without requiring continuous manual monitoring. The entire framework is implemented using Python and OpenCV with deep learning libraries to achieve efficient real-time processing while maintaining low computational overhead. Experimental evaluation demonstrates that the proposed system achieves an object detection accuracy of approximately 92%, with high precision and low latency, making it suitable for intelligent traffic surveillance, public safety monitoring, industrial security, smart city infrastructure, parking management, restricted-area protection, and emergency response systems. By integrating advanced object detection, automated risk analysis, and instant mobile alert mechanisms into a unified intelligent surveillance platform, the proposed system enhances situational awareness, reduces response time, and contributes to the development of reliable AI-driven smart surveillance solutions for modern security applications.

Keywords

Intelligent Surveillance Object Detection YOLO YOLOv3 YOLOv8 Deep Learning Computer Vision Real-Time Monitoring Automated Mobile Alerts SMS Notification Twilio Risk Assessment Distance Estimation Speed Estimation Traffic Surveillance Smart Security Public Safety Artificial Intelligence.

Citation of this Article

Carlo Miguel Garcia, & Kevin Angelo Torres. (2026). Intelligent Surveillance Using YOLO-Based Object Detection and Automated Mobile Alert System. Current Journal of Engineering and Science Research. 3(6), 51-65. Article DOI: https://doi.org/10.47001/CJESR/2026.306006

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