Authors : Aman Shinde; Achal Pawale; Sakshi Patil; Aditya Mohite; Dr. Jyoti B. R.

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/3zwu7cph

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DOI : https://doi.org/10.38124/ijisrt/25dec365

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Abstract : Modern security systems rely mainly on CCTV’s. Whether in schools, offices, shops, or public places, cameras are installed everywhere to help monitor movement and prevent incidents. The majority of CCTV systems, however, continue to function passively. They simply record video and display it on a screen, leaving everything else to human attention. This type of monitoring eventually becomes unreliable due to people’s natural tendency to lose focus, become fatigued, or just be unable to focus on multiple screens at once. In the last few years, the rapid growth of artificial intelligence—especially in computer vision—has opened new possibilities for automating surveillance. Today’s algorithms can detect faces, extract patterns, compare them to known individuals, and provide instant alerts. This reduces the need for continuous enhances safety through human monitoring. The purpose of this project, SentinelAI, is to add “intelligence” to standard security cameras. what is does is it detects the faces and marks whether its known or unknown face. It uses open source tools like Python, dlib, OpenCV, Node.js, and MongoDB, making it affordable and easy to modify. Modern SntinelAI aims to bridge the gap between traditional surveillance and modern intelligent monitoring without requiring expensive hardware or commercial software. Index Terms—Face Recognition, CCTV Surveillance, Machine Learning, Computer Vision, Deep Learning, OpenCV, dlib, RealTime Monitoring, Intelligent Video Surveillances.

Keywords : Face Recognition, CCTV Surveillance, Machine Learning, Computer Vision, Deep Learning, OpenCV, Dlib, RealTime Monitoring, Intelligent Video Surveillance.

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