Real-Time Detection of Suspicious Behaviors and Theft Prevention Using an Artificial Intelligence Solution

Abstract

This research develops a real-time shoplifting detection system using dual-stream deep learning that combines video analysis with human pose estimation. The system achieves 92.45% accuracy in detecting suspicious retail behaviors, enabling proactive theft prevention through automated surveillance and immediate alert generation