Artificial Intelligence in Cyber-Physical Security and Intelligent Surveillance: A Comprehensive Survey
Reshma Abhang1, Pramod Jadhav*2, Sonal Jamdade3, Amol Kadam4, Aradhana Thorat5, Ajit R. Patil6
1Reshma Abhang, Department of Computer Science and Business Systems, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
2Dr. Pramod Jadhav, Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
3Sonal Jamdade, Department of Computer Science and Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
4Dr. Amol Kadam, Department of Computer Science and Business Systems, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
5Aradhana Thorat, Department of Computer Science and Business Systems, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune (Maharashtra), India.
6Dr. Ajit R. Patil, Department of Computer Engineering, Bharati Vidyapeeth College of Engineering, Lavale, Pune, Maharashtra, India.
Manuscript received on 01 September 2026 | Revised Manuscript received on 06 September 2026 | Manuscript Accepted on 15 September 2026 | Manuscript published on 30 September 2026 | PP: 7-12 | Volume-15 Issue-10, September 2026 | Retrieval Number: 100.1/ijitee.K130815111026 | DOI: 10.35940/ijitee.K1308.15111026
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Modern times require smart solutions for crime detection to enhance public security levels because crime detection remains a fundamental challenge. Traditional surveillance relies on human monitoring but has weaknesses due to time consumption and human error. The automated crime detection system runs on machine learning and deep learning platforms using Convolutional Neural Networks and Recurrent Neural Networks, along with YOLO, Faster R-CNN, and the Azure Face API. The AI models perform two functions: detecting suspicious activities and identifying weapons and faces, and recognising patterns of criminal behaviour. The combination of local servers and cloud platforms powered by AI detects crimes precisely in real time through enhanced monitoring, improves accuracy, and prevents false alarms, while also enabling broader crime-prevention capabilities.
Keywords: Crime Detection, Cyber-Physical Systems, Machine Learning, Deep Learning, Smart Surveillance, Object Detection.
Scope of the Article: Computer Science and Engineering