Video anomaly detection with both normal and anomaly memory modules-Reference-Cited by-同舟云学术

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Video anomaly detection with both normal and anomaly memory modules

Published:2024-07-22 Issue: Volume: Page:
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Zhang Liang,Li Shifeng,Luo Xi,Liu Xiaoru,Zhang Ruixuan

Funder

National Natural Science Foundation of China

Science and Technology Research Project of the Department of Education of Liaoning Province

Social Science Planning Fund of Liaoning Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00371-024-03584-z.pdf

Reference44 articles.

1. Abati, D., Porrello, A., Calderara, S., Cucchiara, R.: Latent space autoregression for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 481–490 (2019)

2. Chang, Y., Tu, Z., Xie, W., Yuan, J.: Clustering driven deep autoencoder for video anomaly detection. In: Proceedings of the European Conference on Computer Vision, pp. 329–345 (2020)

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

4. Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2720–2727 (2013)

5. Luo, W., Liu, W., Gao, S.: A revisit of sparse coding based anomaly detection in stacked RNN framework. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 341–349 (2017)

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