MC-MIL: video surveillance anomaly detection with multi-instance learning and multiple overlapped cameras
Author:
Funder
CAPES
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00521-024-09611-3.pdf
Reference28 articles.
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3. Feng J-C, Hong F-T, Zheng W-S (2021) Mist: multiple instance self-training framework for video anomaly detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 14009–14018
4. Asad M, Jiang H, Yang J, Tu E, Malik AA (2022) Multi-stream 3d latent feature clustering for abnormality detection in videos. Appl Intell 52(1):1126–1143
5. Ren J, Xia F, Liu Y, Lee I (2021) Deep video anomaly detection: opportunities and challenges. In: 2021 international conference on data mining workshops (ICDMW), pp 959–966. IEEE
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