CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection (ECCV 2020)

10 Minutes Presentation || 1 minute Presentation || Video Results on UCF-Crime dataset || More Video Results || Paper PDF || Supplementary Material

Other Related Publications from my Project on Anomaly Detection

• A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels

  • Evaluated on: UCF-Crime, ShanghaiTech and UCSD Ped2 anomaly datasets.
  • Venue: Signal Processing Letters (SPL) Journal
  • PDF [IEEEXplore] [Arxiv]
  • Authors: M. Z. Zaheer, A. Mahmood, S.H. Shin, S. I. Lee

• Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection

  • Evaluated on: UCF-Crime and ShanghaiTech anomaly datasets.
  • Venue: Computer Vision and Pattern Recognition Workshops (CVPRW), 2020
  • CVPR talk [Link1] [Link2]
  • Workshop on Learning from Unlabeled Videos [Link]
  • PDF [Arxiv] [LUV Website]
  • Authors: M. Z. Zaheer, J. Lee, M. Astrid, A. Mahmood, S. I. Lee

• Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm

  • Evaluated on: UCSD Pedestrian2, Caltech256 and MNIST for anomalies and outliers detection.
  • Venue: Conference on Computer Vision and Pattern Recognition (CVPR), 2020
  • CVPR talk [Link1] [Link2]
  • Paper PDF [Link1] [Link2]
  • Results video [Link]
  • Authors: M. Z. Zaheer, J. Lee, M. Astrid, A. Mahmood, S. I. Lee

• Ensemble Grid Formation to Detect Potential Anomalous Regions Using Context Encoders

  • Evaluated on: Self-recorded dataset for anomaly detection in moving-camera videos
  • Venue: International Conference on Control, Automation and Systems (ICCAS), 2018
  • Paper PDF [Link]
  • Results videos [Video1] | [Video2] | [Video3]
  • Authors: M. Z. Zaheer, M. Astrid, S. I. Lee, H. C. Shin

CLAWS Net Results and Comparisons

• AUC Performance on UCF-Crime
  • 83.03 % AUC of the ROC curve
  • Comparison provided against Hassan et al., Lu et al., Sultani et al., Zaheer et al., Zhong et al., and SRF.

• AUC Performance on ShanghaiTech
  • 89.67 % AUC of the ROC curve
  • Comparison provided against Zaheer et al., Zhong et al., and SRF.

• ROC Performance
  • Numerical data of the CLAWS Net ROC plots is provide in an excel file. [Link]

False Alarm Rate (FAR)

Several researchers inquired about the FAR of CLAWS Net and SRF approaches on UCF-Crime dataset, as it is a also a popular evaluation metric in the existing literature. Therefore, I am updating the FAR here:

• A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels, SPL, 2020.
  • FAR : 0.13
• Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection, ECCV, 2020.
  • FAR : 0.12

Anomaly Datasets

• UCF-Crime [Link]
• Shanghai Tech [Link]
  • Shannghai Tech anomaly dataset is orgioanlly a one-class training dataset
  • The two-class split used in our paper (proposed by Zhong et al. CVPR19) can be downloaded here. [Link]
• UCSD Ped2 [Link1] [Link2]

Updates

[22/08/2020] Our project is still ongoing. The code will be released after completion of the project.
[02/08/2020] CLAWS Net ECCV 2020 github page created.
[21/04/2021] False Alarm Rates of CLAWS Net and SRF are being mentioned on the github page.

Misc.

If you have any query, please feel free to contact Zaigham through mzz.pieas /@/ gmail/./com

@inproceedings{zaheer2020claws,
  title={CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection},
  author={Zaheer, Muhammad Zaigham and Mahmood, Arif and Astrid, Marcella and Lee, Seung-Ik},
  booktitle={European Conference on Computer Vision},
  pages={358--376},
  year={2020},
  organization={Springer}
}

@article{zaheer2020self,
  title={A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels},
  author={Zaheer, Muhammad Zaigham and Mahmood, Arif and Shin, Hochul and Lee, Seung-Ik},
  journal={IEEE Signal Processing Letters},
  volume={27},
  pages={1705--1709},
  year={2020},
  publisher={IEEE}
}

@inproceedings{cleaning2020zaheer,
  title={Cleaning label noise with clusters for minimally supervised anomaly detection},
  author={Zaheer, Muhammad Zaigham and Lee, Jin-Ha and Astrid, Marcella and Mahmood, Arif and Lee, Seung-Ik},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
  year={June 2020}
}