Tan, Yi Xuan (2023) Real time junction recognition using image matching. Final Year Project, UTAR.
Abstract
In an age of advanced navigation technology, urban road complexity still poses challenges, leading to missed turns and potential hazards. This research endeavors to mitigate these issues by developing a computer vision-based intersection detection system. The project leverages the synergy of Google Directions API and Google Street View (GSV) API to automate route and junction coordination retrieval. The study focuses on a neighborhood in Westlake, Kampar, UTAR, where real-world urban complexity is scaled down for testing. The objective is to instill driver confidence and safety by providing timely and accurate junction notifications. The deliverables include automated route retrieval, junction coordinates, and a mechanism to filter and compare video frames, thereby improving navigation in complex urban environments. By integrating Google Directions API and GSV, our project aims to revolutionize the way drivers navigate unfamiliar roads. Our system not only enhances safety but also streamlines the navigation process by reducing cognitive load. The intelligent frame filtering mechanism represents a significant contribution to the efficiency of the system, ensuring that drivers receive timely and accurate notifications. Overall, this research stands as a testament to our commitment to improving urban navigation and safety through innovative computer vision solutions.
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