Detection of Unusual Activity in Surveillance Video Scenes Based on Deep Learning Strategies

  • Muthana S. Mahdi Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq
  • Amer Jelwy Mohammed Dewan Al-Waqf Al-Sunni, Baghdad, Iraq
  • Abdulghafor Abdulghafour waedallah Presidency of Mustansiriyah University, Baghdad, Iraq
Keywords: Abnormal activity, Human-computer interaction, deep-learning strategies, Automated detection, activity analysis, surveillance scenes


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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[1] Ruff, L., Vandermeulen, R. A., Gornitz, N., Binder, A., Muller, E., & Kloft, M. Deep support vector data description for unsupervised and semi-supervised anomaly detection. In Proceedings of the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning, Long Beach, CA, USA,2019.
[2] Fan, Y., Wen, G., Li, D., Qiu, S., Levine, M. D., & Xiao, F. Video anomaly detection and localization via Gaussian mixture fully convolutional variational autoencoder. Computer Vision and Image Understanding, 102920, 2020.
[3] Gkountakos, K., Ioannidis, K., Tsikrika, T., Vrochidis, S., & Kompatsiaris, I. A Crowd Analysis Framework for Detecting Violence Scenes. In Proceedings of the 2020 International Conference on Multimedia Retrieval (pp.276-280), 2020.
[4] Lin, W., Hasenstab, K., Cunha, G. M., & Schwartzman, A.Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment. Scientific Reports, 10(1), 1-11, 2020.
[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.‏
[7] Kamoona, A. M., Gostar, A. K., Bab-Hadiashar, A., & Hoseinnezhad, R. Sparsity-Based Naive Bayes Approach for Anomaly Detection in Real Surveillance Videos. In 2019 International Conference on Control, Automation and Information Sciences (ICCAIS) (pp. 1-6), 2019.
[8] P.Bhagya Divya, S.Shalini, R.Deepa, Baddeli Sravya Reddy, “Inspection of suspicious human activity in the crowdsourced areas captured in surveillance cameras”, International Research Journal of Engineering and Technology (IRJET), December 2017.
[9] Jitendra Musale, Akshata Gavhane, Liyakat Shaikh, Pournima Hagwane, Snehalata Tadge, “Suspicious Movement Detection and Tracking of Human Behavior and Object with Fire Detection using A Closed Circuit TV (CCTV) cameras ”, International Journal for Research in Applied Science & Engineering Technology (IJRASET) Volume 5 Issue XII December 2017.
[10] Elizabeth Scaria, Aby Abahai T and Elizabeth Isaac, “Suspicious Activity Detection in Surveillance Video using Discriminative Deep Belief Network”, International Journal of Control Theory and Applications Volume 10, Number 29 -2017.
[11] Javier Abellan-Abenza, Alberto Garcia-Garcia, Sergiu Oprea, David Ivorra-Piqueres, Jose Garcia-Rodriguez “Classifying Behaviours in Videos with Recurrent Neural Networks”, International Journal of Computer Vision and Image Processing, December 2017.
[12] U.M.Kamthe, C.G.Patil “Suspicious Activity Recognition in Video Surveillance System”, Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018.
[13] Zahraa Kain, Abir Youness, Ismail El Sayad, Samih Abdul-Nabi, Hussein Kassem, “ Detecting Abnormal Events in University Areas ”, International Conference on Computer and Application,2018.
[14] Tian Wanga, Meina Qia, Yingjun Deng, Yi Zhouc, Huan Wangd, Qi Lyua, Hichem Snoussie, “Abnormal event detection based on analysis of movement information of video sequence”, Article-Optik, vol- 152, January-2018.
[15] Kwang-Eun Ko, Kwee-Bo Sim“Deep convolutional framework for abnormal behavior detection is a smart surveillance system.”Engineering Applications of Artificial Intelligence,67 (2018).
[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.
[19] G. Sreenu and M. A. Saleem Durai “Intelligent video surveillance: a review through deep learning techniques for crowd analysis”, Journal Big Data,2019.
[20] Gurav, S. S., Godbole, B. B., & Sonale, M. S. Improved accuracy of suspicious activity detection in surveillance video. International journal of engineering and advanced technology, 9(3), 267-270, 2020.‏
[21] K. Kavikuil and Amudha, J., “Leveraging deep learning for anomaly detection in video surveillance”, Advances in Intelligent Systems and Computing,2019.
[22] Sudarshana Tamuly, C. Jyotsna, Amudha J, “Deep Learning Model for Image Classification”, International Conference on Computational Vision and Bio-Inspired Computing (ICCVBIC),2019.
[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.
[24] S. Hasen and A. Abdulhadi, “Influence of a Rotating Frame on the Peristaltic Flow of a Rabinowitsch Fluid Model in an Inclined Channel”, JQCM, vol. 12, no. 1, pp. Math Page 21 -, Feb. 2020.
[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.
How to Cite
Mahdi, M., Mohammed, A., & waedallah, 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 -.
Computer article

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