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Sultani_Real-World_Anomaly_Detection_CVPR_2018_paper.pdf
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🔗 Source: openaccess.thecvf.com
📊 Size: 3.23 MB
👤 Author: Waqas Sultani, Chen Chen, Mubarak Shah
⬇️ Downloads: 201

Summary

Real-world anomaly detection in surveillance videos is a pressing need due to the increasing number of cameras and the lack of human monitors. This paper proposes a deep multiple instance ranking framework to learn anomalies from weakly labeled training videos, where only video-level labels are available. The approach treats normal and anomalous videos as bags and video segments as instances, and learns an anomaly ranking model to predict high anomaly scores for anomalous segments. A new large-scale dataset of 128 hours of surveillance videos with 13 realistic anomalies is introduced, which can be used for both anomaly detection and activity recognition tasks. Experimental results show that the proposed method achieves significant improvement over state-of-the-art approaches, and the dataset serves as a challenging benchmark for future work.

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Real-world anomaly detection in surveillance videos is a pressing need due to the increasing number of cameras and the lack of human monitors.

Technical Information

  • File Format: PDF
  • File Size: 3.23 MB
  • Pages: 10
  • Language: EN
  • Author: Waqas Sultani, Chen Chen, Mubarak Shah
  • Total Downloads: 201
  • Last Updated: 3 hours ago

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