Detection of unusual activity in surveillance video scenes based on deep learning strategies
المؤلفون المشاركون
Mahdi, Muthanna S.
Muhammad, Amer Jelwy
Abd al-Ghafur, Abd al-Ghafur Waed Allah
المصدر
al-Qadisiyah Journal for Computer Science and Mathematics
العدد
المجلد 13، العدد 4 (31 ديسمبر/كانون الأول 2021)، ص ص. 1-9، 9ص.
الناشر
جامعة القادسية كلية علوم الحاسوب و تكنولوجيا المعلومات
تاريخ النشر
2021-12-31
دولة النشر
العراق
عدد الصفحات
9
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 8-9
رقم السجل
BIM-1475044
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر