Fast video anomaly detection via context-aware shortcut exploration and abnormal feature distance learning

Author:

Park ChaewonORCID,Kim DonghyeongORCID,Cho MyeongAhORCID,Kim MinjungORCID,Lee MinseokORCID,Park Seungwook,Lee SangyounORCID

Publisher

Elsevier BV

Reference45 articles.

1. M.Z. Zaheer, J.-h. Lee, M. Astrid, S.-I. Lee, Old is gold: Redefining the adversarially learned one-class classifier training paradigm, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 14183–14193.

2. W. Liu, W. Luo, D. Lian, S. Gao, Future frame prediction for anomaly detection–a new baseline, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6536–6545.

3. Integrating prediction and reconstruction for anomaly detection;Tang;Pattern Recognit. Lett.,2020

4. T.-N. Nguyen, J. Meunier, Hybrid Deep Network for Anomaly Detection, in: 30th British Machine Vision Conference, 2019.

5. H. Park, J. Noh, B. Ham, Learning memory-guided normality for anomaly detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 14372–14381.

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