Real-time anomaly detection for ‘Remote’ bus stop surveillance using unsupervised conditional generative adversarial networks-Reference-Cited by-同舟云学术

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Real-time anomaly detection for ‘Remote’ bus stop surveillance using unsupervised conditional generative adversarial networks

Published:2024-05-19 Issue:25 Volume:36 Page:15799-15813
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Xi Beihao,Chen Qingkui

Funder

National Natural Science Foundation of China

The Shanghai Key Science and Technology Project

Ministry of Education Doctoral Fund of Ph.D. Supervisor of China

Shanghai Key Science and Technology Project in Information Technology Field

Shanghai Leading Academic Discipline Project

Shanghai Engineering Research Center Project

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00521-024-09911-8.pdf

Reference32 articles.

1. Islam A, Long C, Radke R (2021) A hybrid attention mechanism for weakly-supervised temporal action localization. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 1637–1645

2. Kiran BR, Thomas DM, Parakkal R (2018) An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. J Imaging 4(2):36

3. Chalapathy R, Chawla S (2019) Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407

4. Abati D, Porrello A, Calderara S, Cucchiara R (2019) Latent space autoregression for novelty detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 481–490

5. Hasan M, Choi J, Neumann J, Roy-Chowdhury AK, Davis LS (2016) Learning temporal regularity in video sequences. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 733–742

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