Browsing by Author "Peiris, R"

Now showing 1 - 5 of 5

Embargo
Computer Vision for Autonomous Driving
(IEEE, 2021-12-09) Kanchana, B; Peiris, R; Perera, D; Jayasinghe, D; Kasthurirathna, D
Computer vision in self-driving vehicles can lead to research and development of futuristic vehicles that can mitigate the road accidents and assist in a safer driving environment. By using the self-driving technology, the riders can be roamed to their destinations without using human interaction. But in recent times self-driving vehicle technology is still at the early stage. Mostly in the rushed areas like cities it becomes challenging to deploy such autonomous systems because even a small amount of data can cause a critical accident situation. In Order to increase the autonomous driving conditions computer vision and deep learning-based approaches are tended to be used. Finding the obstacles on the road and analyzing the current traffic flow are mainly focused areas using computer vision-based approaches. As well as many researchers using deep learning-based approaches like convolutional neural networks to enhance the autonomous driving conditions. This research paper focused on the evaluation of computer vision used in self-driving vehicles.
Embargo
COVID 19 Navigator Taxi Application for Urban Mobility during Pandemic Period
(IEEE, 2022-02-23) Wickramarathne, J; Perera, D; Kanchana, B. C; Peiris, R
This research paper focuses on increasing the awareness between taxi users and can help to protect themselves in COVID-19 and take precautions. Since 2019 the global pandemic of Covid-19 is spreading at an astonishing rate and causes a negative impact for economic, social, and cultural factors. Government agencies are warning people to reduce the transportation and to maintain social distance. To stop the spread of COVID-19 required to identify persons who are susceptible to infection and need to trace the COVID-19 positive first contacts. Most people are reduced to using public transportations and taxi services due to unidentifiable health conditions in earlier users. The proposed approach can be used to track the taxi drivers and their passengers previous COVID-19 status as well as navigate the safest route by showing the COVID-19 contamination areas. By using this approach users can be aware of earlier users of the taxi service and COVID-19 status of the taxi driver or passenger before taking the trip as well as if they get touch with any COVID-19 patients, can take immediate precautions. This application helps to increase the usage of taxis by making users trust and confidence against COVID-19 infection. By testing with real users our system was able to trace down 45 passengers and 16 drivers within 3 months.
Open Access
IOT Based Smart Microgreen Sprouter
(Springer, Cham, 2022-01) Rankothge, V; Kehelella, P; Perera, D; Kanchana, B. C; Peiris, R; Madushan, K
This research paper focuses on enhancing indoor farming technologies with an emerging technology: Internet of Things (IoT). The proposed approach creates a microgreen sprouter unit that automates the process of monitoring and providing optimum growing conditions with the minimum human supervision. This model can be used to achieve healthy growth of microgreen by providing ideal ventilation, moisture, humidity, light, and temperature levels, which prevents sprouts from ultraviolet radiation and pest attacks. Users can track the growth rate of sprouts and change the moisture, humidity, light and temperature levels. Our prototype implementation has been tested for mung-beans sprouts and validated for its accuracy and efficiency
Embargo
Review On Hand Gesture Recognition for Bengali Sign Language
(IEEE, 2022-02-23) Perera, D; Kanchana, B. M; Peiris, R; Madushan, K; Kasthurirathna, D
Communication becomes difficult when interaction between the disabled and the general public are required. People with disabilities of various races communicate using various sign languages. For persons who are deaf or hard of hearing sign language is their primary mode of communication. However, the majority of our community does not understand sign language, taking them out in public is incredibly challenging. In order to make sign language understandable to the general public, computer vision-based methods are now widely used. Recognition of hand gesture is one of the computer vision based technologies for recognizing sign language, and it is attracting a lot of attention from analysis. For a long time, it has been a popular research area. In the area of hand gesture recognition in computer vision, some recent research has achieved outstanding improvements by employing deep learning techniques. In this paper we have discussed the previous research methods, technologies, datasets and models used in Bengal sign language gestures that are interconnected in terms of achieving a successful result. Therefore, this review article tried to reveal the independent techniques which are used to overcome the challenges in research.
Embargo
Review On Hand Gesture Recognition for Bengali Sign Language
(IEEE, 2022-04-14) Perera, D; Kanchana, B; Peiris, R; Madushan, K; Kasthurirathna, D
Communication becomes difficult when interaction between the disabled and the general public are required. People with disabilities of various races communicate using various sign languages. For persons who are deaf or hard of hearing sign language is their primary mode of communication. However, the majority of our community does not understand sign language, taking them out in public is incredibly challenging. In order to make sign language understandable to the general public, computer vision-based methods are now widely used. Recognition of hand gesture is one of the computer vision based technologies for recognizing sign language, and it is attracting a lot of attention from analysis. For a long time, it has been a popular research area. In the area of hand gesture recognition in computer vision, some recent research has achieved outstanding improvements by employing deep learning techniques. In this paper we have discussed the previous research methods, technologies, datasets and models used in Bengal sign language gestures that are interconnected in terms of achieving a successful result. Therefore, this review article tried to reveal the independent techniques which are used to overcome the challenges in research.