Rapid urbanization and increasing vehicle numbers have led to a significant rise in road accidents, posing serious threats to human life and economic stability. Traditional accident detection systems rely on manual reporting, causing delays in emergency response. To address this, computer vision integrated with intelligent transportation systems offers an effective solution. This study analyzes pre-2018 approaches to traffic accident detection using video surveillance. The proposed system automatically detects accidents by analyzing traffic camera footage through feature extraction, motion analysis, and pattern recognition. It identifies abnormal vehicle behavior such as sudden speed drops, collisions, and irregular trajectories. Classical computer vision techniques like optical flow, background subtraction, edge detection, and machine learning methods such as Support Vector Machines (SVM) and decision trees are used. The system includes preprocessing, object detection, trajectory tracking, and classification of normal and abnormal events, supported by mathematical modeling and threshold-based decisions. Results show high detection accuracy with low false alarms, especially in controlled environments like highways and intersections. However, challenges remain in real-time implementation due to lighting, occlusion, and camera angle issues. Future work suggests incorporating deep learning and multi-sensor data fusion to improve performance. In conclusion, computer vision-based accident detection is a promising approach to enhancing road safety and reducing response time, contributing to the advancement of smart transportation systems.
Nimal Perera, Tharindu Jayasinghe· International Journal of Mod...· 0 citations
Cloud infrastructure automation has emerged as a pivotal component in modern cloud computing, enabling efficient resource management, rapid deployment, and enhanced scalability. This paper provides a comprehensive survey of current technologies in cloud infrastructure automation, including Infrastructure as Code (IaC), configuration management, continuous integration/continuous deployment (CI/CD), and containerization. It explores the integration of serverless architectures with Function as a Service (FaaS) and Infrastructure as a Service (IaaS), highlighting the challenges and solutions in their hybrid implementation. The study also delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) in fostering predictive and self-healing cloud systems. Furthermore, it addresses the complexities introduced by multi-cloud configurations and the management tools like Kubernetes and Terraform that aid in their orchestration. By analyzing these technologies, the paper offers insights into future directions and research opportunities in cloud infrastructure automation.
Nimal Perera, Tharindu Jayasinghe· International Journal of Art...· 0 citations
In today’s dynamic business environment, organizations are increasingly relying on multi-cloud strategies to achieve flexibility, cost efficiency, and scalability. However, managing and optimizing IT costs while ensuring optimal performance across multiple cloud environments remains a complex challenge. This paper explores the concept of an Elastic Data Platform (EDP) as a solution for multi-cloud IT cost optimization and performance. By leveraging the inherent elasticity of cloud resources, this architecture provides the ability to scale data infrastructure efficiently while maintaining high performance levels. We discuss the key design principles of an EDP, including data distribution, workload optimization, auto-scaling, and cost analytics, and how these can be implemented across multiple cloud providers. Additionally, we analyze real-world use cases, benefits, and challenges associated with this architecture. This paper aims to provide insights into how businesses can optimize both costs and performance in a multi-cloud environment using an Elastic Data Platform.
Nimal Perera· International Journal of Dat...· 0 citations