Detection of unusual activity in surveillance video scenes based on deep learning strategies
Joint Authors
Mahdi, Muthanna S.
Muhammad, Amer Jelwy
Abd al-Ghafur, Abd al-Ghafur Waed Allah
Source
al-Qadisiyah Journal for Computer Science and Mathematics
Issue
Vol. 13, Issue 4 (31 Dec. 2021), pp.1-9, 9 p.
Publisher
University of al-Qadisiyah College of computer Science and Information Technology
Publication Date
2021-12-31
Country of Publication
Iraq
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
In today's world, abnormal activity indicates threats and risks to others.
An anomaly can be defined as something that deviates from what is expected, common, or normal.
Because it is difficult to continuously monitor public spaces, intelligent video surveillance is necessary.
When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far.
Different methods are in place using the above combinations to help distinguish various suspicious activities from the live tracking of footage.
Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough.
In an academic setting, a deep learning Technique is utilized to detect normal or abnormal behavior and sends an alarm message to the appropriate authorities if suspicious activity is predicted.
Monitoring is frequently carried out by extracting successive frames from a video.
The framework is split into two sections.
The features are calculated from video frames in the first phase, and the classifier predicts whether the class is suspicious or normal in the second part based on the obtained features.
This paper proposes an effective method to design a system that automatically detects any unexpected or abnormal circumstance and alerts the appropriate authority and it can be used in both indoor and outdoor settings in an academic area.
The proposed system was able to achieve an accuracy rate of 95.3 percent.
American Psychological Association (APA)
Mahdi, Muthanna S.& Muhammad, Amer Jelwy& Abd al-Ghafur, Abd al-Ghafur Waed Allah. 2021. Detection of unusual activity in surveillance video scenes based on deep learning strategies. al-Qadisiyah Journal for Computer Science and Mathematics،Vol. 13, no. 4, pp.1-9.
https://search.emarefa.net/detail/BIM-1475044
Modern Language Association (MLA)
Mahdi, Muthanna S.…[et al.]. Detection of unusual activity in surveillance video scenes based on deep learning strategies. al-Qadisiyah Journal for Computer Science and Mathematics Vol. 13, no. 4 (2021), pp.1-9.
https://search.emarefa.net/detail/BIM-1475044
American Medical Association (AMA)
Mahdi, Muthanna S.& Muhammad, Amer Jelwy& Abd al-Ghafur, Abd al-Ghafur Waed Allah. Detection of unusual activity in surveillance video scenes based on deep learning strategies. al-Qadisiyah Journal for Computer Science and Mathematics. 2021. Vol. 13, no. 4, pp.1-9.
https://search.emarefa.net/detail/BIM-1475044
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 8-9
Record ID
BIM-1475044