Department of Computer Science;

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Browsing Department of Computer Science; by Subject "Computer Vision"

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    A Hybrid Swarm Intelligence Convolution Neural Network for Object Detection and Tracking
    (Lead City University, 2023-12) Afiss Emiola KAREEM
    In recent years, analysis and interpretation of video sequences to detect and track objects of interest had become an active research field in computer vision and image processing. Despite significant efforts in object detection and tracking, an efficient method which provides high computational efficiency has not been developed. Hence, in this work a Hybrid Particle Swarm Optimization Convolution Neural Network (CNN-HPSO) technique was developed to improve computational efficiency in object detection and tracking. The video datasets (MP4 and AVi video formats) used in this work were obtained from a conventional online database and on a real-time basis from YouTube. Multiple frames sampled from the video clips were pre-processed and then segmented. An Enhanced Particle Swarm Optimization (HPSO) was formulated from standard PSO and was applied to Convolution Neural Network (CNN). CNN- HPSO technique was used for edge detection and extraction of the boundary of the image and the object tracking was finally carried out. The work was implemented using MatLab R2016 software. The average results of CNN-HPSO, CNN-PSO and CNN on the videos with MP4 format yielded processing time, accuracy, precision, FPR, sensitivity and specificity of 165.89s, 97.08%, 98.41%, 7.75%, 97.82%, and 92.25%; 179.52s, 94.25%, 96.99%, 10.37%, 95.23% and 89.62%; and 189.19s, 89.95%, 93.64%, 15.56%, 91.95.33% and 84.44% respectively. For the videos in AVi format, CNN-HPSO, CNN-PSO and CNN produced similar average results with processing time, accuracy, precision, FPR, sensitivity, and specificity of 185.09s, 96.62%, 98.23%, 7.80%, 97.34% and 92.19%; 198.24s, 94.83%, 97.62%, 8.56%, 95.46% and 91.43%; and 216.59s, 91.30%, 93.98%, 15.09%, 93.67% and 84.91% respectively. In this research, a CNN-HPSO with associated high computational efficiency was developed. The developed technique can be used for solving other related optimization problems. Keywords: Deep Learning Algorithms, Computer Vision, Moving Objects, Video Frame, Object Segmentation Word Count: 293
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    Real-time Surveillance Network System for Traffic Monitoring
    (Lead City University, Ibadan, 2024-12) Abiodun AKANNI
    As urban populations continue to surge, the prevalence of traffic-related issues escalates, leading to heightened concerns over public safety, property damage, and various offenses posing significant risks to both life and assets. Traditional solutions have relied heavily on infrastructure-integrated systems, which are often costly to install and maintain, lacking flexibility and scalability. This study aims to develop an approach to address these challenges by creating a low-cost, real-time vehicular monitoring and reporting system. The system employs readily available technology, built on a foundation of electronic architecture, encompassing a Network Unit (Tunnel Server), Mobile Unit (Mobile App), and number plate detection unit. The process involves establishing an HTTP connection between the Tunnel Server and Mobile App. A tunnelling server, a web application, and a number plate detection unit collaborate to detect license plates in real-time. ML5.js and OpenCV.js are employed to process captured frames, identify objects, and extract license plate numbers. The number plate was identified through the utilisation of the find number plate function (Open.js). This function operates by analysing the image, converting it to grayscale, performing edge detection, and subsequently identifying contours to determine the presence of a number plate based on its distinctive feature. The system's performance is evaluated in terms of response time (80%), stability (70%), and usability (84%). The system demonstrates exceptional compatibility with various operating systems and browsers and boasts good scalability and throughput. This research marks a significant technological achievement in the realm of web and mobile applications, computer vision, and artificial intelligence. The developed system successfully detects license plate numbers, promising enhanced public safety, property protection, and traffic management. It is therefore recommendations that future enhancements such as expanding its object recognition capabilities and maintaining a robust testing and quality assurance process to ensure its continued excellence. Keywords: Computer Vision, Electronic, Infrastructure-Integrated Systems, License Plates, Number Plate Detection, Object Recognition, OpenCV.js, Tunnel Server Word Count: 293