AI-ML Enabled Intelligent Video Analysis System for Automated Shoplifting Detection in Retail Environments
Nancy Kumari¹, Alka Kumari², Aryan Kumar³, Abhishek Kumar⁴, Mr. Hemant Kumar Yadav⁵, Mr. Badal Bhushan⁶
1B. Tech (CSE) -Final Year Student,
Dept Computer Science & Engineering, IIMT College of Engineering, Greater Noida
(Email id : kumarinancy129@gmail.com )
2B. Tech (CSE) -Final Year Student,
Dept Computer Science & Engineering, IIMT College of Engineering, Greater Noida
(Email id : mansi03alka01@gmail.com )
3B. Tech (CSE) -Final Year Student,
Dept Computer Science & Engineering, IIMT College of Engineering, Greater Noida
(Email id : kumararyan7541@gmail.com )
4B. Tech (CSE) -Final Year Student,
Dept Computer Science & Engineering, IIMT College of Engineering, Greater Noida
(Email id : abhigupta8292@gmail.com )
5,6Project Supervisor, Assistant Professor, Dept. of Computer Science & Engineering,
IIMT College of Engineering, Greater Noida,, Greater Noida, UP, India
(Email id : hemant.yadav@iimtindia.net , bhushan.badal@gmail.com )
Abstract- Retail shoplifting and loss prevention represent critical operational challenges for the global retail industry, costing over $100 billion annually. Traditional security approaches relying on manual surveillance and rule-based detection systems demonstrate significant limitations in real-time detection capability, false-positive rate management, and operational scalability. To address these limitations, this paper proposes an AI-ML enabled intelligent video analysis system designed specifically for automated shoplifting detection and loss prevention in retail environments. The proposed system integrates Convolutional Neural Networks (CNNs) for spatial feature extraction, Long Short-Term Memory (LSTM) networks for temporal pattern analysis, YOLO object detection architecture, and behavioral anomaly detection modules. The effectiveness of the proposed approach is evaluated using 1,500+ hours of custom-annotated retail surveillance footage from diverse retail locations and scenarios. Experimental results demonstrate 92.1% precision in suspicious activity detection with only 2.3% false-positive rate, representing substantial improvements over traditional rule-based systems and baseline deep learning approaches. Real-time processing capability is maintained with latency of 45-60 milliseconds per frame, enabling practical deployment across retail networks without noticeable delays.
Keywords- Retail Security, Shoplifting Detection, Video Analysis, Deep Learning, YOLO, LSTM, Behavioral Anomaly Detection, Loss Prevention, Real-Time Detection, Surveillance Systems, CNN, Feature Extraction, Risk Assessment
