Deep Hybrid Architecture for Suspicious Action Detection in Video Surveillance
Author:
Affiliation:
1. Aligarh Muslim University,Department of Computer Science,Aligarh,Uttar Pradesh,India,202002
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10389811/10389819/10389884.pdf?arnumber=10389884
Reference21 articles.
1. RGB+D and deep learning-based real-time detection of suspicious event in Bank-ATMs
2. Deep Learning-Based Video Action Recognition: A Review
3. IBaggedFCNet: An Ensemble Framework for Anomaly Detection in Surveillance Videos
4. Online learning of contexts for detecting suspicious behaviors in surveillance videos
5. Untargeted white-box adversarial attack to break into deep learning based COVID-19 monitoring face mask detection system
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1. Enhancing public safety: a hybrid Conv_Trans-OptBiSVM approach for real-time abnormal behavior detection in crowded environments;Signal, Image and Video Processing;2024-09-04
2. EASAD: efficient and accurate suspicious activity detection using deep learning model for IoT-based video surveillance;International Journal of Information Technology;2024-06-03
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