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Multiple Parallel Activity Detection and Recognition to Avoid COVID-19 Spread-Out
15th International Conference on Information Technology and Applications, ICITA 2021 ; 350:207-217, 2022.
Article in English | Scopus | ID: covidwho-1844322
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. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 15th International Conference on Information Technology and Applications, ICITA 2021 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 15th International Conference on Information Technology and Applications, ICITA 2021 Year: 2022 Document Type: Article