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Computer Vision Used to Monitor The Youth during The Pandemic Covid-19
Abstract
Computer Vision is a field that studies methods for capturing numerical or symbolic information. Some of the computer vision processes are image capture, image enhancement, segmentation, feature extraction, and clarification. AI-based Computer Vision technology allows the public to carry out more optimal surveillance to deal with Covid-19. During the Covid-19 pandemic, many people have adopted new normal habits by implementing health protocols. But not a few of our people do not understand the importance of implementing health protocols. Where after this year's Eid homecoming, not a few of our people have made trips to their hometown villages. In this case, there is already a prohibition from the Government not to make the Eid homecoming trip. These communities can be at risk of transmitting the Covid-19 virus to their families in their hometowns. This study describes how Computer Vision works in helping the community to monitor travelers from the city to minimize the spread of Covid-19. The paper wa presented using the literature review method. From the description result using the literature review, it has result that computer vision technology has enormous potential in spreading countermeasures Covid-19.
ResearchGate has not been able to resolve any citations for this publication.
Perkembangan teknologi yang terkait dengan pemrosesan menggunakan komputer demikian pesatnya, salah satunya adalah image (citra) atau disebut Pengolahan Citra Digital (Digital Image Processing). Agar dapat diolah dengan komputer digital, maka suatu citra harus dipresentasikan secara numerik dengan nilai-nilai diskri. Secara matematis, citra merupakan fungsi kontinyu dengan intensitas cahaya pada bidang dua dimensi. Agar dapat diolah dengan komputer digital, maka suatu citra harus dipresentasikan secara numerik dengan nilai-nilai diskrit. Computer Vision merupakan cabang ilmu Pengolahan Citra Digital yang memungkinkan komputer dapat melihat seperti manusia, sehingga dapat mengambil keputusan, melakukan aksi, dan mengenali suatu Objek. Bentuk implementasi dari Computer Vision adalah Face Detection, dimana salah satu metodenya Haar-like Features. Metode ini menghitung perbedaan jumlah setiap piksel pada daerah persegi panjang (rectangular) yang berdekatan pada lokasi tertentu dalam
jendela deteksi. Berdasarkan pengujian yang dengan menggunakan citra statis maupun citra dinamis, didapatkan kesimpulan bahwa metode haar-like sangat cocok digunakan untuk face detection dengan obyek citra statis maupun citra dinamis. Dengan menggunakan indikasi dari face detection untuk citra statis maupun citra dinamis maka jumlah orang akan diketahui. Pada pengujian dengan citra statis error 28% terjadi pada saat gambar berisi 7 orang.
The Internet of Things (IoT) is a new paradigm that combines aspects and technologies coming from different approaches. Ubiquitous computing, pervasive computing, Internet Protocol, sensing technologies, communication technologies, and embedded devices are merged together in order to form a system where the real and digital worlds meet and are continuously in symbiotic interaction. The smart object is the building block of the IoT vision. By putting intelligence into everyday objects, they are turned into smart objects able not only to collect information from the environment and interact/control the physical world, but also to be interconnected, to each other, through Internet to exchange data and information. The expected huge number of interconnected devices and the significant amount of available data open new opportunities to create services that will bring tangible benefits to the society, environment, economy and individual citizens. In this paper we present the key features and the driver technologies of IoT. In addition to identifying the application scenarios and the correspondent potential applications, we focus on research challenges and open issues to be faced for the IoT realization in the real world.
This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. • Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry • A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking • More than 70 algorithms are described in sufficient detail to implement • More than 350 full-color illustrations amplify the text • The treatment is self-contained, including all of the background mathematics • Additional resources at www.computervisionmodels.com
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