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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