A Dynamic Convolutional Generative Adversarial Network for Video Anomaly Detection
Author:
Funder
Natural Science Foundation of Hebei Province
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-022-07096-7.pdf
Reference51 articles.
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2. Santhosh, K.K.; Dogra, D.P.; Roy, P.P.: Anomaly detection in road traffic using visual surveillance: a survey. ACM Comput. Surv. (CSUR) 53(6), 1–26 (2020). https://doi.org/10.1145/3417989
3. Li, B.; Leroux, S.; Simoens, P.: Decoupled appearance and motion learning for efficient anomaly detection in surveillance video. Comput. Vis. Image Underst. 210, 103249 (2021). https://doi.org/10.1016/j.cviu.2021.103249
4. Chen, D.; Wang, P.; Yue, L.; Zhang, Y.; Jia, T.: Anomaly detection in surveillance video based on bidirectional prediction. Image Vis. Comput. 98, 103915 (2020). https://doi.org/10.1016/j.imavis.2020.103915
5. Hamdi, S.; Bouindour, S.; Snoussi, H.; Wang, T.; Abid, M.: End-to-end deep one-class learning for anomaly detection in uav video stream. J. Imaging 7(5), 90 (2021). https://doi.org/10.3390/jimaging7050090
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