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Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

Esam A. AlQaralleh1, Fahad Aldhaban2, Halah Nasseif2, Malek Z. Alksasbeh3, Bassam A. Y. Alqaralleh2,*

1 School of Engineering, Princess Sumaya University for Technology, Amman, 11941, Jordan
2 MIS Department, College of Business Administration, University of Business and Technology, Jeddah, 21448, Saudi Arabia
3 CIS Department, Faculty of Information Technology, Al Hussein bin Talal University, Ma'an, 71111, Jordan

* Corresponding Author: Bassam A. Y. Alqaralleh. Email: email

Computers, Materials & Continua 2022, 72(3), 5593-5605. https://doi.org/10.32604/cmc.2022.026666

Abstract

Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes. The use of deep learning (DL) technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification. The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention. Human action recognition (HAR) is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level. The advancements of the DL models help to accomplish improved recognition performance. In this view, this paper presents a smart deep-based human behavior classification (SDL-HBC) model for real-time video surveillance. The proposed SDL-HBC model majorly aims to employ an adaptive median filtering (AMF) based pre-processing to reduce the noise content. Also, the capsule network (CapsNet) model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam optimizer. Finally, the differential evolution (DE) with stacked autoencoder (SAE) model is applied for the classification of human activities in the intelligent video surveillance system. The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset. The experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.

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APA Style
AlQaralleh, E.A., Aldhaban, F., Nasseif, H., Alksasbeh, M.Z., Alqaralleh, B.A.Y. (2022). Smart deep learning based human behaviour classification for video surveillance. Computers, Materials & Continua, 72(3), 5593-5605. https://doi.org/10.32604/cmc.2022.026666
Vancouver Style
AlQaralleh EA, Aldhaban F, Nasseif H, Alksasbeh MZ, Alqaralleh BAY. Smart deep learning based human behaviour classification for video surveillance. Comput Mater Contin. 2022;72(3):5593-5605 https://doi.org/10.32604/cmc.2022.026666
IEEE Style
E.A. AlQaralleh, F. Aldhaban, H. Nasseif, M.Z. Alksasbeh, and B.A.Y. Alqaralleh "Smart Deep Learning Based Human Behaviour Classification for Video Surveillance," Comput. Mater. Contin., vol. 72, no. 3, pp. 5593-5605. 2022. https://doi.org/10.32604/cmc.2022.026666



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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