Violence Detection In Surveillance Videos Using Deep Learning
Moaaz, M., Mohamed, E. (2020). Violence Detection In Surveillance Videos Using Deep Learning. EKB Journal Management System, 2(2), 1-6. doi: 10.21608/fcihib.2020.42233.1003
Mostafa Mohamed Moaaz; Ensaf Hussein Mohamed. "Violence Detection In Surveillance Videos Using Deep Learning". EKB Journal Management System, 2, 2, 2020, 1-6. doi: 10.21608/fcihib.2020.42233.1003
Moaaz, M., Mohamed, E. (2020). 'Violence Detection In Surveillance Videos Using Deep Learning', EKB Journal Management System, 2(2), pp. 1-6. doi: 10.21608/fcihib.2020.42233.1003
Moaaz, M., Mohamed, E. Violence Detection In Surveillance Videos Using Deep Learning. EKB Journal Management System, 2020; 2(2): 1-6. doi: 10.21608/fcihib.2020.42233.1003

Violence Detection In Surveillance Videos Using Deep Learning

النشرة المعلوماتية في الحاسبات والمعلومات
Article 1, Volume 2, Issue 2 - Serial Number 20200202, October 2020, Page 1-6  XML PDF (570.39 K)
Document Type: المقالة الأصلية
DOI: 10.21608/fcihib.2020.42233.1003
View on SCiNiTO View on SCiNiTO
Authors
Mostafa Mohamed Moaaz1; Ensaf Hussein Mohamed email orcid 2
1Computer Science. Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
2Computer Science, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo. Egypt.
Abstract
Nowadays computer technologies are flowering especially the artificial intelligence field. It lives its prosperous years. Recently it closes the gap between humans and machines with the facilitation of supporting decisions. One of these gaps is the surveillance cameras labors' attentiveness and the lack of instantaneous detection of violence actions on the scenes of such cameras. In this paper we present an end to end deep neural network to detect the violence scenes in the surveillance cameras, the proposed system composed of set of phases. It extracts a set of selectively distributed frames of the video clip and preforms a spatio-temporal features and passes them to a fully connected neural to classify they video to violence or non-violence action. The model is evaluated on different datasets; like Real Life Violence Situations aka RLVS and Hockey Fight Detection datasets. The accuracy was 92% and 94.5% respectively, which outperformed the previous related works.
Keywords
Anomaly Detection; Deep Learning; Surveillance Video; Feature Extraction; Classification
References
Statistics
Article View: 1,158
PDF Download: 811