MAVAD: Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos

This paper introduces the first audio-visual dataset for traffic anomaly detection called MAVAD, taken from real-world scenes, with a diverse range of illumination conditions. In addition, a novel anomaly detection method is proposed which combines visual and audio features extracted from video sequences by means of cross-attention. We demonstrate that the addition of audio improves anomaly detection performance by up to 5.2%. Moreover, the impact of image anonymization is evaluated, showing only a minor decrease in performance averaging at 1.7%.