Abstract
COVID-19 has been affecting people around the globe. It is affecting almost every country currently, according to the World Health Organization (WHO). This virus is transmitted to another person if an asymptomatic person makes close contact with the everyday person. There is no cure for this virus, and the only solution is social distancing and avoids the people doing these activities. In this paper, we proposed a system for detecting and recognizing the activities that violate social distancing. These activities involve handshakes and hugging. We implement a system that is capable of detecting and identifying multiple parallel activities. Temporal features are extracted for similar activities in 16 frames. We use the two models for this purpose: Faster RCNN for the detection and ResNet-50 to recognize the activities. First, Faster RCNN detects the group of people and that region of interest ROI saved and passes to the ResNet-50 to recognize the activities. We also generated our dataset on the local environment in multiple parallel activities. This system achieves the accuracy of 95.03% for the detection of testing dataset and recognition of multiple parallel activities 92.88% accuracy accomplished. The system used different public datasets and generated some local datasets for handshake and hugging activities.
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Acknowledgements
We want to offer our sincere thanks to the National Center of Artificial Intelligence for ultimately supporting our exploration work. The authors also want to acknowledge the appreciation of all group members (associates and the executives) and association (KICS) for their help, commitment, specialized meetings, and information sharing exertion for this. Without their support and encouragement, this research work could not be accomplished.
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Ubaid, M.T., Khan, M.Z., Khan, M.U.G., Rehman, A., Ayesha, N. (2022). Multiple Parallel Activity Detection and Recognition to Avoid COVID-19 Spread-Out. In: Ullah, A., Anwar, S., Rocha, Á., Gill, S. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 350. Springer, Singapore. https://doi.org/10.1007/978-981-16-7618-5_18
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DOI: https://doi.org/10.1007/978-981-16-7618-5_18
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