Abstract
Due to the Covid-19 epidemic, there is now a high need for social seclusion. As several studies have shown, it is possible to limit the transmission of Covid-19 by keeping appropriate social distances. Open CV with deep learning is used to detect the distance between individuals to lessen the effect of the coronavirus epidemic, as we describe in this paper. Individuals should be warned to stay a safe distance from one other using a video broadcast, it was suggested. To put the model to the test, we use CCTV camera footage to gather video clips, which we then feed into CNN models that have already been trained. We are attempting to use YOLOv3 algorithm to recognize pedestrians on the road. Video file now transformed top-down perspective distance measurement from the 2D plane once pedestrian recognition is complete. Social distance violation is the visible distance between any two persons less than the intended length. For every person, the distance between them is highlighted in blue or green if it seems to be more than the predicted distance. Our current model is being tested with prerecorded videos of people strolling the street to verify its correctness. According to the finding, the suggested system is capable of detecting various degrees of social distance between different characters given movie. The same approach may be used future to identify social distance violations in real-time applications.
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Pillalamarri, S.S., Saikumar, K., Al-Ameedee, S.A., Abdul Kadeem, S.R., Hussein, M.J. (2023). An Open CV-Based Social Distance Violation Identification Using a Deep Learning Technique . In: Sharma, D.K., Peng, SL., Sharma, R., Jeon, G. (eds) Micro-Electronics and Telecommunication Engineering . Lecture Notes in Networks and Systems, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-19-9512-5_55
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