Detection of Unusual Activity in Surveillance Video Scenes Based on Deep Learning Strategies
DOI:
https://doi.org/10.29304/jqcm.2021.13.4.858Keywords:
Abnormal activity, Human-computer interaction, deep-learning strategies, Automated detection, activity analysis, surveillance scenesAbstract
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.
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References
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[5] Ramchandran, A., & Sangaiah, A. K. Unsupervised deep learning system for local anomaly event detection in crowded scenes. Multimedia Tools and Applications, 1-21, 2019.
[6] Krizhevsky, A., Sutskever, I., & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105, 2012.
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[16] Yuke Li “A Deep Spatiotemporal Perspective for Understanding Crowd Behavior”, IEEE Transactions on Multimedia, Vol. 20, NO. 12, December 2018.
[17] Asma Al Ibrahim, Gabriel Abosamra, Mohamed Dahab “Real-Time Anomalous Behavior Detection of Students in Examination Rooms Using Neural Networks and Gaussian Distribution”, International Journal of Scientific and Engineering Research, October 2018.
[18] Dinesh Jackson Samuel R, Fenil E, Gunasekaran Manogaran, Vivekananda G.N, Thanjaivadivel T, Jeeva S, Ahilan A, “Real-time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM”, The International Journal of Computer and Telecommunications Networking,2019.
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[23] A. Ali, M. RASHEED, S. SHIHAB, T. RASHID, A. Sabri, and S. Abed Hamed, “An Effective Color Image Detecting Method for Colorful and Physical Images”, JQCM, vol. 13, no. 1, pp. Comp Page 88 -, Mar. 2021.
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[25] A. Hameed Khaleel, “Automated ovarian masses extraction in CT images based on division of image”, JQCM, vol. 6, no. 1, pp. 11-27, Aug. 2017.
[26] K. Neamah Hussein, “Video Frames Edge Detection of Red Blood Cells: A Performance Evaluation”, JQCM, vol. 10, no. 1, pp. Comp Page 16 - 27, Jan. 2018.
[27] S. Turky, A. Ahmed AL-Jumaili, and R. Hasoun, “Deep Learning Based On Different Methods For Text Summary: A Survey”, JQCM, vol. 13, no. 1, pp. Comp Page 26-, Mar. 2021.
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Published
2021-12-07
How to Cite
Mahdi, M. S., Mohammed, A. J., & waedallah, A. A. (2021). Detection of Unusual Activity in Surveillance Video Scenes Based on Deep Learning Strategies. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(4), Comp Page 1 – 9. https://doi.org/10.29304/jqcm.2021.13.4.858
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Section
Computer Articles
