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arXiv · 2305.15084

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

Błażej Leporowski·Arian Bakhtiarnia·Nicole Bonnici·Adrian Muscat·Luca Zanella·Yiming Wang·Alexandros Iosifidis
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Abstract

We introduce the first audio-visual dataset for traffic anomaly detection taken from real-world scenes, called MAVAD, with a diverse range of weather and illumination conditions. In addition, we propose a novel method named AVACA that combines visual and audio features extracted from video sequences by means of cross-attention to detect anomalies. We demonstrate that the addition of audio improves the performance of AVACA by up to 5.2%. We also evaluate the impact of image anonymization, showing only a minor decrease in performance averaging at 1.7%.

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Błażej Leporowski, Arian Bakhtiarnia, Nicole Bonnici, Adrian Muscat, Luca Zanella, Yiming Wang, Alexandros Iosifidis. 2023-05-24. Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos. https://arxiv.org/abs/2305.15084

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