Deep-Learning-Based Action and Trajectory Analysis for Museum Security Videos

Abstract

Recent advancements in deep learning and video analysis, combined with the efficiency of contemporary computational resources, have catalyzed the development of advanced real-time computational systems, significantly impacting various fields. This paper introduces a cutting-edge video analysis framework that was specifically designed to bolster security in museum environments. We elaborate on the proposed framework, which was evaluated and integrated into a real-time video analysis pipeline. Our research primarily focused on two innovative approaches: action recognition for identifying potential threats at the individual level and trajectory extraction for monitoring museum visitor movements, serving the dual purposes of security and visitor flow analysis. These approaches leverage a synergistic blend of deep learning models, particularly CNNs, and traditional computer vision techniques. Our experimental findings affirmed the high efficacy of our action recognition model in accurately distinguishing between normal and suspicious behaviors within video feeds. Moreover, our trajectory extraction method demonstrated commendable precision in tracking and analyzing visitor movements. The integration of deep learning techniques not only enhances the capability for automatic detection of malevolent actions but also establishes the trajectory extraction process as a robust and adaptable tool for various analytical endeavors beyond mere security applications.