Abstract

In today's world, daily life increasingly depends on vision-based advanced technologies, which enhance the reliability and convenience of human lifestyles. Among these technologies, vision-based Human Activity Recognition (HAR) stands out as a comprehensive and challenging field of study, with broad exploration and practical applications. HAR systems are designed to identify diverse human actions under varying environmental conditions.Vision-based activity recognition plays a crucial role in a wide range of applications, including user interface design, robot learning, security surveillance, healthcare, video searching, abnormal activity detection, and human-computer interaction. This study focuses on recognizing various human activities in real-world settings, highlighting the importance of consistency and credibility in the results.To achieve this, data was collected from multiple sources and processed using three distinct models—Convolutional Neural Network (CNN), VGG-16, and ResNet50—to identify the most effective approach for activity recognition. Among these, a specific architectural CNN model was further evaluated for its ability to capture human activity features in specific video sequences. The training, validation, and testing phases utilized a comprehensive dataset comprising 56,690 images. Remarkably, the proposed system achieved an impressive accuracy of 96.23% after 30 epoch running and low validation loss illustrate its effectively recognition each feature.

References

• Manaf, A. and Singh, S., 2021, May. Computer Vision-based Survey on Human Activity Recognition System, Challenges and Applications. In 2021 3rd International Conference on Signal Processing and Communication (ICPSC) (pp. 110-114). IEEE.
• Vahora, S.A. and Chauhan, N.C., 2019. Deep neural network model for group activity recognition using contextual relationship. Engineering Science and Technology, an International Journal, 22(1), pp.47-54.
• Agarwal, P. and Alam, M., 2020. A lightweight deep learning model for human activity recognition on edge devices. Procedia Computer Science, 167, pp.2364-2373.
• Shikha, M., Kumar, R., Aggarwal, S. and Jain, S., 2020. Human activity recognition. International Journal of Innovative Technology and Exploring Engineering, 9(7), pp.903-905.
• Wei, L. and Shah, S.K., 2017, February. Human Activity Recognition using Deep Neural Network with Contextual Information. In VISIGRAPP (5: VISAPP) (pp. 34-43).
• Ravi, D., Wong, C., Lo, B. and Yang, G.Z., 2016, June. Deep learning for human activity recognition: A resource efficient implementation on low-power devices. In 2016 IEEE 13th international conference on wearable and implantable body sensor networks (BSN) (pp. 71-76). IEEE.
• Hayat, A., Morgado-Dias, F., Bhuyan, B.P. and Tomar, R., 2022. Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches. Information, 13(6), p.275.
• Wu, D., Sharma, N. and Blumenstein, M., 2017, May. Recent advances in video-based human action recognition using deep learning: A review. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2865-2872). IEEE.
• Khan, I.U., Afzal, S. and Lee, J.W., 2022. Human activity recognition via hybrid deep learning based model. Sensors, 22(1), p.323.
• Amrutha, C.V., Jyotsna, C. and Amudha, J., 2020, March. Deep learning approach for suspicious activity detection from surveillance video. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 335-339). IEEE.
• Jobanputra, C., Bavishi, J. and Doshi, N., 2019. Human activity recognition: A survey. Procedia Computer Science, 155, pp.698-703.
• Gu, F., Chung, M.H., Chignell, M., Valaee, S., Zhou, B. and Liu, X., 2021. A survey on deep learning for human activity recognition. ACM Computing Surveys (CSUR), 54(8), pp.1-34.
• Ann, O.C. and Theng, L.B., 2014, November. Human activity recognition: a review. In 2014 IEEE international conference on control system, computing and engineering (ICCSCE 2014) (pp. 389-393). IEEE.
• Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L. and Amirat, Y., 2015. Physical human activity recognition using wearable sensors. Sensors, 15(12), pp.31314-31338.
• Martínez-Mascorro, G.A., Abreu-Pederzini, J.R., Ortiz-Bayliss, J.C. and Terashima-Marín, H., 2020. Suspicious behavior detection on shoplifting cases for crime prevention by using 3D convolutional neural networks. arXiv preprint arXiv:2005.02142.
• Kaluza, B., 2013. Detection of Anomalous and Suspicious Behavior Patterns from Spatio-Temporal Agent Traces.
• Khare, S., Sarkar, S. and Totaro, M., 2020, June. Comparison of Sensor-Based Datasets for Human Activity Recognition in Wearable IoT. In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT) (pp. 1-6). IEEE.
• Ahmad, Z., Illanko, K., Khan, N. and Androutsos, D., 2019, August. Human action recognition using convolutional neural network and depth sensor data. In Proceedings of the 2019 International Conference on Information Technology and Computer Communications (pp. 1-5).
• Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L. and Amirat, Y., 2015. Physical human activity recognition using wearable sensors. Sensors, 15(12), pp.31314-31338.
• Subetha, T. and Chitrakala, S., 2016, February. A survey on human activity recognition from videos. In 2016 international conference on information communication and embedded systems (ICICES) (pp. 1-7). IEEE.
• Jegham, I., Khalifa, A.B., Alouani, I. and Mahjoub, M.A., 2020. Vision-based human action recognition: An overview and real world challenges. Forensic Science International: Digital Investigation, 32, p.200901.
• Sarkar, P. K., & Abdullah, A. B. M. (2022). Diagnosing Suspects by Analyzing Human Behavior to Prevent Crime by Using Deep and Machine Learning.
• Arunnehru, J., Chamundeeswari, G. and Bharathi, S.P., 2018. Human action recognition using 3D convolutional neural networks with 3D motion cuboids in surveillance videos. Procedia computer science, 133, pp.471-477.
• Varshney, P., Harsh Tyagi, N.K., Lohia, A.K. and Girdhar, P., 2021. A Deep Learning Based Approach to Detect Suspicious Weapons. Proceedings http://ceur-ws. org ISSN, 1613, p.0073.
• Khattar, L., Kapoor, C. and Aggarwal, G., 2021, January. Analysis of Human Activity Recognition using Deep Learning. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 100-104). IEEE.