Unsupervised Anomaly Detection for Traffic Surveillance Based on Background Modeling

@article{Wei2018UnsupervisedAD,
  title={Unsupervised Anomaly Detection for Traffic Surveillance Based on Background Modeling},
  author={Jiayi Wei and Jianfei Zhao and Yanyun Zhao and Zhicheng Zhao},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2018},
  pages={129-1297}
}
Most state-of-the-art anomaly detection methods are specific to detecting anomaly for pedestrians and cannot work without adequate normal training videos. Recently, there is a growing demand for detecting anomalous vehicles in traffic surveillance videos. However, the biggest challenge in this task is the lack of labeled datasets for training supervised models. By examining the resemblances of anomalous vehicles, we find it reasonable to label a vehicle as anomaly if it stays still in the video… CONTINUE READING

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