Presenter(s)

    Vatsa Sanjay Patel

    Comments

    Presentation: 1:40-2:00, LTC Studio

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    Description

    In this study, we thoroughly investigate the reliability of computer vision object detection systems in real-world traffic scenarios, particularly focusing on challenging weather conditions. Traditional evaluation methods often fall short in addressing the complexities of dynamic traffic environments, which is increasingly important with the advancement of autonomous vehicle technologies. Our research specifically examines how these algorithms perform in adverse weather like fog, rain, snow, and sun glare, recognizing the significant impact of weather on their accuracy. We emphasize that a system performing well in clear weather may struggle in adverse conditions. Our study includes detailed analyses of different architectural approaches, aiming to enhance traffic monitoring, vehicle tracking, and object tracking. Ultimately, our goal is to enhance transportation safety and efficiency by advancing robust computer vision systems for future autonomous and intelligent transportation technologies.

    Publication Date

    4-17-2024

    Project Designation

    Graduate Research

    Primary Advisor

    Van Tam Nguyen

    Primary Advisor's Department

    Computer Science

    Keywords

    Stander Symposium, College of Arts and Sciences

    Institutional Learning Goals

    Scholarship; Practical Wisdom; Critical Evaluation of Our Times

    Evaluation of Object Detection Methods in Inclement Weather

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