UIT-Anomaly: A Modern Vietnamese Video Dataset for Anomaly Detection

Abstract

Anomaly detection in videos is of utmost importance for numerous tasks in the field of computer vision. We introduce the UIT-Anomaly dataset captured in Vietnam with a total duration of 200 minutes. It contains 224 videos with six different types of anomalies. Moreover, we apply a method for weakly supervised video anomaly detection, called Robust Temporal Feature Magnitude learning (RTFM) based on feature magnitude learning to detect abnormal snippets. The approached method yields competitive results, compared to other state-of-the-art algorithms using publicly available datasets such as ShanghaiTech and UCF–Crime.