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As security threats evolve, conventional surveillance systems often become victims of suspicious activities. The present study describes the building of an AI-driven Smart Surveillance System for the monitoring and analysis of unusual behavior in a real-time setting by the YOLOv8 high-performing deep learning algorithm. In contrast to the traditional methods, which by and large depend on manual monitoring or rule-based techniques, this system is designed to find itself in an environment that learns through observation and recognition of potentially threatening patterns regarding the event with an outstanding degree of accuracy and speed. Additionally, the application of the latest object detection and deep learning techniques heightens security surveillance because they minimize response time and enhance situational awareness. Evaluation results demonstrate the effectiveness of the proposed system, achieving a detection accuracy of 96%, with significant improvements in processing speed and false alarm reduction compared to traditional approaches. This research presents a scalable and responsive solution for smart surveillance, offering enhanced situational awareness and proactive security monitoring through deep learning advancements.

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A Smart Surveillance Framework for Real-Time Suspicious Activity Detection and Automated Alert Generation Using YOLOv8

  • 会议论文

  • 发表日期：

    2026年

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  • Anurag De,
  • Venkata Naga Durga Sowmya Kollipara,
  • Gautam Pal,
  • Meghana Bellamkonda,
  • Sai Surya Nikhil Vissapragada

摘要

As security threats evolve, conventional surveillance systems often become victims of suspicious activities. The present study describes the building of an AI-driven Smart Surveillance System for the monitoring and analysis of unusual behavior in a real-time setting by the YOLOv8 high-performing deep learning algorithm. In contrast to the traditional methods, which by and large depend on manual monitoring or rule-based techniques, this system is designed to find itself in an environment that learns through observation and recognition of potentially threatening patterns regarding the event with an outstanding degree of accuracy and speed. Additionally, the application of the latest object detection and deep learning techniques heightens security surveillance because they minimize response time and enhance situational awareness. Evaluation results demonstrate the effectiveness of the proposed system, achieving a detection accuracy of 96%, with significant improvements in processing speed and false alarm reduction compared to traditional approaches. This research presents a scalable and responsive solution for smart surveillance, offering enhanced situational awareness and proactive security monitoring through deep learning advancements.