Faculty of Computing

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Subject: search.filters.subject.Surveillance System

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Now showing 1 - 4 of 4
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    A Surveillance System Controlling Covid-19 in Office Environments
    (IEEE, 2022-12-09) Bandara, P A D; Perera, P D D S; Perera, N P D D D; De Silva, P. N.; Kasthuriarachchi, S; Rajapaksha, U.U.S
    COVID-19 is one of the pandemic diseases that has hit the world including Sri Lanka. He has a virus that became the target of bids to stop its spread. Including the implementation of health protocols, to provide information about the spread of the virus emergency response, detection services for suspicious persons infected with the virus, and programs to contain the spread of the virus ensuring that the whole of Sri Lanka gets vaccinated. Here, the research focuses on the minimal spread of the face mask in the office environment an identification system that uses a deep learning model that prioritizes object recognition for the identification of employees who wear a face mask and detects social distancing and crowd gathering, if any if there is a violation, it will inform via a voice notification. Loss of Smell after the next component. One person can use one disposable card to check the smell of sniffing. Each disposable card has QR codes, and all QR codes are encrypted by adding data. The user scans the QR code on their ticket and then scratches off and smelled the smelling area and selected the corresponding scent on the disposable card. Employee company attendance is a proposed automated attendance system using facial recognition. Because it requires minimal human influence and offers a high level of accuracy and marking employee attendance and employee body temperature measurement, facial recognition will appear to be a practical option. This system aims to provide a high level of protection. Automated Attendance systems that detect and recognize are safe, fast, and time-consuming savings. This technique can also be used to identify an unknown person.
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    Deep Learning-Based Surveillance System for Coconut Disease and Pest Infestation Identification
    (IEEE, 2021-12-07) Vidhanaarachchi, S. P.; Akalanka, P. K. G. C.; Gunasekara, R. P. T. I.; Rajapaksha, H. M. U.D; Aratchige, N. S.; Lunugalage, D; Wijekoon, J. L
    The coconut industry which contributes 0.8% to the national GDP is severely affected by diseases and pests. Weligama coconut leaf wilt disease and coconut caterpillar infestation are the most devastating; hence early detection is essential to facilitate control measures. Management strategies must reach approximately 1.1 million coconut growers with a wide range of demographics. This paper reports a smart solution that assists the stakeholders by detecting and classifying the disease, infestation, and deficiency for the sustainable development of the coconut industry. It leads to the early detections and makes stakeholders aware about the dispersions to take necessary control measures to save the coconut lands from the devastation. The results obtained from the proposed method for the identifications of disease, pest, deficiency, and degree of diseased conditions are in the range of 88% - 97% based on the performance evaluations.
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    A Geophone Based Surveillance System Using Neural Networks and IoT
    (IEEE, 2020-12-10) Hettigoda, S; Jayaminda, C; Amarathunga, U; Thaha, S; Wijesundara, M; Wijekoon, J
    Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.
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    PublicationEmbargo
    A Geophone Based Surveillance System Using Neural Networks and IoT
    (IEEE, 2020-12-10) Hettigoda, S; Jayaminda, C; Amarathunga, U; Thaha, S; Wijesundara, M; Wijekoon, J
    Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.