REAL-TIME SMART SECURITY SURVEILLANCE USING ADVANCED COMPUTER VISION TECHNIQUE

Abstract

Smart surveillance systems have become essential for ensuring safety and security in modern environments. Traditional surveillance methods rely heavily on manual monitoring, which is inefficient and error-prone. This paper presents a real-time smart security surveillance framework using advanced computer vision techniques to enhance threat detection and situational awareness. The proposed system integrates video acquisition, preprocessing, object detection, tracking, and behavior analysis into a unified architecture. Deep learning–based vision models are employed to achieve high accuracy in realtime scenarios. Experimental evaluation is conducted on benchmark video datasets and real-world surveillance footage. Results demonstrate improved detection accuracy, reduced false alarms, and reliable real-time performance. The framework offers a scalable and intelligent solution for smart security applications.