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

Błażej Leporowski, Arian Bakhtiarnia, Nicole Bonnici, Adrian Muscat, Luca Zanella +2 more
5/24/2023
cs.CV

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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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Cite this paper

@article{leporowski2023audiovisual,
  title  = {Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos},
  author = {Błażej Leporowski and Arian Bakhtiarnia and Nicole Bonnici and Adrian Muscat and Luca Zanella and Yiming Wang and Alexandros Iosifidis},
  year   = {2023},
  eprint = {2305.15084},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.15084v1}
}

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