Suspicious Human Activity Recognition From Surveillance Videos Using Deep Learning-Reference-Cited by-同舟云学术

Suspicious Human Activity Recognition From Surveillance Videos Using Deep Learning

Published:2024 Issue: Volume:12 Page:105497-105510
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mohamed Zaidi Monji¹^ORCID,Avelino Sampedro Gabriel²^ORCID,Almadhor Ahmad³^ORCID,Alsubai Shtwai⁴^ORCID,Al Hejaili Abdullah⁵,Gregus Michal⁶,Abbas Sidra⁷^ORCID

Affiliation:

1. Department of Electrical Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia

2. School of Management and Information Technology, De La Salle—College of Saint Benilde, Manila, Philippines

3. Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia

4. College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

5. Computer Science Department, Faculty of Computers & Information Technology, University of Tabuk, Tabuk, Saudi Arabia

6. Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia

7. Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan

Funder

Deanship of Research and Graduate Studies, King Khalid University, through the Large Research Project

Slovak Research and Development Agency

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10620187.pdf?arnumber=10620187

Reference25 articles.

1. A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillance

2. Detection of Real-world Fights in Surveillance Videos

3. Deep Learning Approach for Suspicious Activity Detection from Surveillance Video

4. Real-World Anomaly Detection in Surveillance Videos

5. Unsupervised Anomaly Detection for Traffic Surveillance Based on Background Modeling