Channel based approach via faster dual prediction network for video anomaly detection-Reference-Cited by-同舟云学术

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Channel based approach via faster dual prediction network for video anomaly detection

Published:2024-03-04 Issue: Volume: Page:
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Li Hongjun^ORCID,Shen Xulin^ORCID,Sun Xiaohu^ORCID,Wang Yunlong^ORCID,Li Chaobo^ORCID,Chen Junjie^ORCID

Funder

National Natural Science Foundation of China

Jiangsu Industry University Research Cooperation Project

Nantong Science and Technology Program

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11042-024-18707-w.pdf

Reference37 articles.

1. Kanagaraj K, Priya GG (2021) A new 3D convolutional neural network (3D-CNN) framework for multimedia event detection. SIViP 15(4):779–787

2. Mnasri Z, Rovetta S, Masulli F (2022) Anomalous sound event detection: A survey of machine learning based methods and applications. Multimedia Tool Appl 81(4):5537–5586

3. Ilyas Z, Aziz Z, Qasim T, Bhatti N, Hayat MF (2021) A hybrid deep network based approach for crowd anomaly detection. Multimedia Tools Appl 80(16):24053–24067

4. Hao Y, Li J, Wang N, Wang X, Gao X (2022) Spatiotemporal consistency-enhanced network for video anomaly detection. Pattern Recogn 121:108232

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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