Research Papers - Dept of Software Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1022

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Subject: search.filters.subject.Computer Vision

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Now showing 1 - 6 of 6
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    Kaizen: Computer Vision Based Interactive Karate Training Platform
    (Institute of Electrical and Electronics Engineers Inc., 2022-11-04) Jayasekara, S. M; Weerasinghe, S. S.; Abayawardana, D.Y.W.; Welagedara, A. R.; Siriwardana, S.E.R.; Koralalage, M. N
    All types of martial arts consist of several forms of combat used in self-defense, which are deeply rooted in many countries. Of all the martial art types, karate is considered the most well-known out of them all. Due to the pandemic situation in Sri Lanka, karate enthusiasts have lost the opportunity to train in a well-guided environment. As a result, even though virtual training came into play, it has continuously proved its ineffectiveness in evaluating the performance and accuracy of the trainees. The main objective of this proposed system is to virtualize the processes of a physical karate dojo. Kaizen - A Computer Vision-Based Interactive Karate Training Platform is a web-based application that functions as a virtual instructor. The proposed system consists of two main core components for Training and Assessments. The karate training component evaluates the techniques against a set of predefined joint angles. The BlazePose model is used for keypoint detection, and Analytic Geometry is used to extract joint angles. It is also integrated with Amazon Polly, a Deep Learning-based Text-To-Speech (TTS) service to produce real-time audio feedback. The assessment component has the capability to evaluate the trainees through a built-in Smart Evaluator based on a Recurrent Neural Network (RNN). Additionally, the capability to manage the assessments supports the instructors in conducting all the assessments virtually, overcoming the barriers in physical training.
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    Continuous American Sign Language Recognition Using Computer Vision And Deep Learning Technologies
    (IEEE, 2022-08-29) Senanayaka, S.A.M.A.S; Perera, R.A.D.B.S; Rankothge, W.; Usgalhewa, S.S.; Hettihewa, H.D
    Sign language is a non-verbal communication method used to communicate between hard of hearing or deaf and ordinary people. Automatic Sign language detection is a complex computer vision problem due to the diversity of modern sign languages and variations in gesture positions, hand and finger form, and body part placements. This research paper aims to conduct a systematic experimental evaluation of computer vision-based approaches for sign language recognition. The present research focuses on mapping non-segmented video streams to glosses to gain insights into sign language recognition. The proposed machine learning model consists of Recurrent Neural Network (RNN) layers such as Long Short-Term Memory (LSTM). The model is implemented using current deep learning frameworks such as Google TensorFlow and Keras API.
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    Computer Vision Enabled Drowning Detection System
    (IEEE, 2021-12-09) Handalage, U; Nikapotha, N; Subasinghe, C; Prasanga, T; Thilakarthna, T; Kasthurirathna, D
    Safety is paramount in all swimming pools. The current systems expected to address the problem of ensuring safety at swimming pools have significant problems due to their technical aspects, such as underwater cameras and methodological aspects such as the need for human intervention in the rescue mission. The use of an automated visual-based monitoring system can help to reduce drownings and assure pool safety effectively. This study introduces a revolutionary technology that identifies drowning victims in a minimum amount of time and dispatches an automated drone to save them. Using convolutional neural network (CNN) models, it can detect a drowning person in three stages. Whenever such a situation like this is detected, the inflatable tube-mounted self-driven drone will go on a rescue mission, sounding an alarm to inform the nearby lifeguards. The system also keeps an eye out for potentially dangerous actions that could result in drowning. This system’s ability to save a drowning victim in under a minute has been demonstrated in prototype experiments' performance evaluations.
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    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.
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    Computer vision based indoor navigation for shopping complexes
    (acm.org, 2020-12-09) Perera, G. S. T; Madhubhashini, K. W. R; Lunugalage, D; Piyathilaka, D. V. S; Lakshani, W. H. U; Kasthurirathna, D
    Smartphone-based indoor navigation systems are frantically required in indoor situations. This limitation of clients is significant. Global Positioning System (GPS) isn't plausible for indoor areas as it gives exceptionally helpless outcomes for indoor restriction. In this research paper, we present a Computer Vision-Based Indoor Navigation System for shopping complexes. Computer vision is used in this system to find the exact location/current location of the user. It contains a mobile android application for positioning, navigating, and displaying the current location for showing on 2D Map. The system will detect the user's position, generate a GIS map, display the shortest path using A* search algorithm, and provides step-by-step direction to the destination using audio instruction for localization with Augmented Reality (AR) map and navigation using mobile phone sensor technologies like accelerometer, gyroscope, and magnetometer. The audio instructions include active guidance for upcoming turns in the traveling path, distance of each section between turns. This system uses a suggestion-based Chabot that uses a trained model to improve the user's experience. Thus, this research expects to build a cost-effective, efficient, and timely response system that will help the users for a smart shopping experience.
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    Computer-Vision Enabled Waste Management System for Green Environment
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Hewagamage, P.; Mihiranga, A.; Perera, D.; Fernando, R.; Thilakarathna, T.; Kasthurirathna, D.
    Waste management has become a critical requirement to maintain a green environment in Sri Lanka as well as other countries. Town councils have to regularly collect different types of wastes to clean cities/towns. Hence managing the waste of the cities is a challenging task. However, most of the urban councils currently use a manual approach to managing waste. However, it results in many difficulties for the people and cleaning staff who involve in the process by following strict guidelines. Issues due to waste contamination, no proper information management of waste collection, and no punctuality in removing waste from the garbage bins are some of the significant issues arising from the manual process. Due to the drawbacks of the manual approach, social issues, environmental issues, health issues can occur easily. This paper proposes a better solution to replace this manual system with an automated system to overcome these issues. Hence, the main objective of this research is to introduce an ICT-based innovative design that can be used to develop an effective waste management system in town councils. In the proposed model, we will introduce a Computer Vision-based smart waste bin system with real-time monitoring that incorporates various technologies such as computer vision, sensor-based IoT devices, and geographical information system (GIS) related technologies. Our proposed solution consists of a waste bin system, which is capable of automated waste segregation. Our design facilitates the admin users to expand the waste bin kit by adding more waste categories in a user-friendly manner, making our product adaptive in any environment. At the same time, waste bins can notify the real-time waste status. Our system generates the optimum collection routing path and displays it in a mobile app using those real-time status details. We also demonstrate a lowcost prototype.