mlsanityData to 7 Oct 2026

What Matters in Autonomous Driving Anomaly Detection: A Weakly Supervised Horizon

arXiv 2408.05562 ↗First posted 10 Aug 2024cs.CVcited by 1

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Abstract (arXiv)

Video anomaly detection (VAD) in autonomous driving scenario is an important task, however it involves several challenges due to the ego-centric views and moving camera. Due to this, it remains largely under-explored. While recent developments in weakly-supervised VAD methods have shown remarkable progress in detecting critical real-world anomalies in static camera scenario, the development and validation of such methods are yet to be explored for moving camera VAD. This is mainly due to existing datasets like DoTA not following training pre-conditions of weakly-supervised learning. In this paper, we aim to promote weakly-supervised method development for autonomous driving VAD. We reorganize the DoTA dataset and aim to validate recent powerful weakly-supervised VAD methods on moving camera scenarios. Further, we provide a detailed analysis of what modifications on state-of-the-art methods can significantly improve the detection performance. Towards this, we propose a "feature transformation block" and through experimentation we show that our propositions can empower existing weakly-supervised VAD methods significantly in improving the VAD in autonomous driving. Our codes/dataset/demo will be released at github.com/ut21/WSAD-Driving

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 10 Aug 2024.

Builds on

19 of 28 references.

References · 19

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  12. 2023Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detectioncited by 88
7 more
  1. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
  2. 2018Temporal Recurrent Networks for Online Action Detectioncited by 79
  3. 2019Unsupervised Traffic Accident Detection in First-Person Videoscited by 69
  4. 2019GODS: Generalized One-class Discriminative Subspaces for Anomaly Detectioncited by 64
  5. 2017Abnormal Event Detection in Videos using Spatiotemporal Autoencodercited by 57
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026CoRE: Weakly Supervised Coarse-to-Fine Risk Evidence Learning in Driving Videoscited by 0