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

arXiv 2305.15084 ↗First posted 24 May 2023cs.CVcited by 0

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Abstract (arXiv)

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%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 27 references that are in the map. First posted on arXiv 24 May 2023.

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References · 22

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10 more
  1. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  2. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  3. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 137
  4. 2021Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learningcited by 44
  5. 2016Automatic tagging using deep convolutional neural networkscited by 39
  6. 2022Self-attention fusion for audiovisual emotion recognition with incomplete datacited by 22
  7. 2020Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditionscited by 13
  8. 2021Audio-visual Representation Learning for Anomaly Events Detection in Crowdscited by 13
  9. 2021Image/Video Deep Anomaly Detection: A Surveycited by 10
  10. 2021Multimedia Datasets for Anomaly Detection: A Reviewcited by 2

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