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  • Open Access

    ARTICLE

    Real Time Feature Extraction Deep-CNN for Mask Detection

    Hanan A. Hosni Mahmoud, Norah S. Alghamdi, Amal H. Alharbi*

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1423-1434, 2022, DOI:10.32604/iasc.2022.020586

    Abstract COVID-19 pandemic outbreak became one of the serious threats to humans. As there is no cure yet for this virus, we have to control the spread of Coronavirus through precautions. One of the effective precautions as announced by the World Health Organization is mask wearing. Surveillance systems in crowded places can lead to detection of people wearing masks. Therefore, it is highly urgent for computerized mask detection methods that can operate in real-time. As for now, most countries demand mask-wearing in public places to avoid the spreading of this virus. In this paper, we are presenting an object detection technique… More >

  • Open Access

    ARTICLE

    Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

    Kathiravan Srinivasan1, Lalit Garg2,*, Debajit Datta3, Abdulellah A. Alaboudi4, N. Z. Jhanjhi5, Rishav Agarwal3, Anmol George Thomas1

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4109-4124, 2021, DOI:10.32604/cmc.2021.016736

    Abstract According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semi-automated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver’s distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an alarm system. Further, this research… More >

  • Open Access

    ARTICLE

    A Deep-CNN Crowd Counting Model for Enforcing Social Distancing during COVID19 Pandemic: Application to Saudi Arabia’s Public Places

    Salma Kammoun Jarraya1,2,*, Maha Hamdan Alotibi1,3, Manar Salamah Ali1

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1315-1328, 2021, DOI:10.32604/cmc.2020.013522

    Abstract With the emergence of the COVID19 virus in late 2019 and the declaration that the virus is a worldwide pandemic, health organizations and governments have begun to implement severe health precautions to reduce the spread of the virus and preserve human lives. The enforcement of social distancing at work environments and public areas is one of these obligatory precautions. Crowd management is one of the effective measures for social distancing. By reducing the social contacts of individuals, the spread of the disease will be immensely reduced. In this paper, a model for crowd counting in public places of high and… More >

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