Action detection using deep learning shoplifting detection framework

Abstract

This project delves into the application of deep learning for action detection, with a specifi cfocu so nidentifyin gshopliftin gbehavior si nretai lenvironments. Th egrowing need for automated surveillance systems that can efficient lya ndaccurate lydetect suspicious activities has motivated this work. Shoplifting action detection is the process of identifying and localizing shoplifting activities in a video by findin gbot hwhere and when an action occurs within a video clip and determining what action is being performed. A key challenge lies in preparing a dataset that reflect sth ecomplexity of real-world scenarios, which was addressed by employing semi-supervised learning techniques. The use of You Only Watch Once version 9 (YOLOv9) object detection model,its tracking function, was instrumental in the automation of labeling and tracking objects within the shoplifting video dataset, ensuring a reliable foundation for action detection. To evaluate the effectivenes so fth esystem ,th eYo uOnl yWatc hOnc eversio n2 (YOWOv2) model was used, conducting comprehensive training and testing across a variety of shoplifting situations. This allowed for a detailed assessment of the model’s ability to recognize and generalize diverse shoplifting actions, even in challenging environments. The results show that the models can detect suspicious behavior, offerin ga promising tool for improving retail security. This work contributes to the broader field of shoplifting detection by providing insights into how deep learning techniques can enhance real-time surveillance and reduce theft in retail settings, with potential applications in other domains of anomaly detection. The YOWOv2-Medium-16-frames model gave the best performance with 54.74% frame mean average precision and 42.67% in video mean average precision.