An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction

TL;DR

This paper proposes TSGAD, a novel human-centric video anomaly detection method using variational autoencoders and trajectory prediction, demonstrating promising results and highlighting the potential of VAEs in this domain.

Contribution

Introduces TSGAD, a new approach combining VAEs and trajectory prediction for pose-based video anomaly detection, with comprehensive experiments showing its effectiveness.

Findings

TSGAD achieves comparable results to state-of-the-art methods.

Using VAEs offers advantages like reduced computational complexity and privacy preservation.

The approach demonstrates the potential of variational autoencoders in human-centric VAD.

Abstract

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.