Object-centric and memory-guided network-based normality modeling for video anomaly detection
Author:
Funder
CSRI, Government of India
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-022-02161-y.pdf
Reference20 articles.
1. Deepak, K., Chandrakala, S., Mohan, C.K.: Residual spatiotemporal autoencoder for unsupervised video anomaly detection. Signal, Image Video Process 15, 215–222 (2021)
2. Del Giorno, A., Bagnell, J. A., Hebert, M.: A discriminative framework for anomaly detection in large videos. In: European Conference on Computer Vision. Springer, pp 334–349 (2016)
3. Dong, F., Zhang, Y., Nie, X.: Dual discriminator generative adversarial network for video anomaly detection. IEEE Access 8, 88170–88176 (2020)
4. Gong, D., Liu, L., Le, V., et al.: Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1705–1714 (2019)
5. Hasan, M., Choi, J., Neumann, J., et al.: Learning temporal regularity in video sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 733–742 (2016)
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