Privacy-preserving Real-world Video Anomaly Detection

Abstract

Video anomaly detection is a significant problem in computer vision that aims to detect unusual or abnormal behaviors in video data that can be used to enhance public safety. Given the widespread deployment of cameras in public areas, video anomaly detection for public safety has become increasingly important in recent years. There are numerous applications, including but not limited to security, traffic monitoring, healthcare, and manufacturing, where video anomaly detection can be useful. However, anomaly detection in nature is an open-set problem that further complicates the task. Moreover, the definition of anomalous behavior may differ in various environments, adding to real-world anomaly detection challenges. On the other hand, addressing ethical issues and privacy concerns related to this task is also crucial. We aim to design an anomaly detection method that uses non-identifiable features such as pose, trajectory, and optical flow to avoid discrimination against distinct minority groups and safeguard the privacy of individuals.