  Video Anomaly Detection (VAD) represents a challenging and prominent research
task within computer vision. In recent years, Pose-based Video Anomaly
Detection (PAD) has drawn considerable attention from the research community
due to several inherent advantages over pixel-based approaches despite the
occasional suboptimal performance. Specifically, PAD is characterized by
reduced computational complexity, intrinsic privacy preservation, and the
mitigation of concerns related to discrimination and bias against specific
demographic groups. This paper introduces TSGAD, a novel human-centric
Two-Stream Graph-Improved Anomaly Detection leveraging Variational Autoencoders
(VAEs) and trajectory prediction. TSGAD aims to explore the possibility of
utilizing VAEs as a new approach for pose-based human-centric VAD alongside the
benefits of trajectory prediction. We demonstrate TSGAD's effectiveness through
comprehensive experimentation on benchmark datasets. TSGAD demonstrates
comparable results with state-of-the-art methods showcasing the potential of
adopting variational autoencoders. This suggests a promising direction for
future research endeavors. The code base for this work is available at
https://github.com/TeCSAR-UNCC/TSGAD.
