Experimental Analysis using Deep Learning Techniques for Safety and Riskless Transport - A Sustainable Mobility Environment for Post Covid-19
| Autor: | D. Selvakarthi, S. Ashwath, D. Sivabalaselvamani, K. Manikandan, A.P. Aswin Kalaivanan, C. Pradeep |
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| Rok vydání: | 2021 |
| Předmět: |
050210 logistics & transportation
2019-20 coronavirus outbreak Coronavirus disease 2019 (COVID-19) Computer science business.industry Distancing Deep learning Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) media_common.quotation_subject Social distance 05 social sciences 02 engineering and technology Work (electrical) Risk analysis (engineering) 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Normality media_common |
| Zdroj: | 2021 6th International Conference on Inventive Computation Technologies (ICICT). |
| DOI: | 10.1109/icict50816.2021.9358749 |
| Popis: | Safety living in the society is the greatest challenge due to COVID-19. Transportation has a crucial role in ensuring the safety of society. To bring back the normality of the society after COVID-19, proper wearing of a mask and strict distancing can play a vital role in the solutions of environment and health. Hence the development of smart and riskless transport with active surveillance of passengers to enforcing the wear of mask and maintaining the social distance can be attained through deep learning algorithms (thermal imaging and sensor technologies). In this proposed work, the various image patterns have analyzed using deep learning algorithms. Passengers' health and count inside the bus are measured using sensor technologies. Hence the development of this model has capable of ensuring social distancing among the people and avoiding the crowd for riskless transport. |
| Databáze: | OpenAIRE |
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