Crowd anomaly detection and localization via deep convolutional model with improved spatio temporal textures
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17375-6.pdf
Reference37 articles.
1. Li X, Li W, Liu B et al (2019) Object and patch based anomaly detection and localization in crowded scenes. Multimed Tools Appl 78:21375–21390. https://doi.org/10.1007/s11042-019-7447-1
2. Venkatraman S, Surendiran B (2020) Adaptive hybrid intrusion detection system for crowd sourced multimedia internet of things systems. Multimed Tools Appl 79:3993–4010. https://doi.org/10.1007/s11042-019-7495-6
3. Hatirnaz E, Sah M, Direkoglu C (2020) A novel framework and concept-based semantic search Interface for abnormal crowd behaviour analysis in surveillance videos. Multimed Tools Appl 79:17579–17617. https://doi.org/10.1007/s11042-020-08659-2
4. Khan MUK, Park H, Kyung C (2019) Rejecting Motion Outliers for Efficient Crowd Anomaly Detection. IEEE Trans Inf Forensics Secur 14(2):541–556. https://doi.org/10.1109/TIFS.2018.2856189
5. Direkoglu C (2020) Abnormal Crowd Behavior Detection Using Motion Information Images and Convolutional Neural Networks. IEEE Access 8:80408–80416. https://doi.org/10.1109/ACCESS.2020.2990355
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