Vehicle Counting and Classification from Traffic Videos

Authors

Presenter(s)

Jaswant Prabhakaran

Comments

Presentation: 2:00-2:20 p.m., Jessie Hathcock Hall 180

Files

Description

Using a trained neural network, counted and classified vehicles in different Vietnamese traffic conditions (day, night, rain, and flash floods). The algorithm automatically tracks and counts the number of vehicles passing through a specific area while also categorizing them as cars, motorcycles, trucks, and buses. This provides an accurate representation of traffic patterns and flow for the specified region in Vietnam, under specific weather conditions and time of day. Another group then integrated this information into a traffic simulation system which allowed for improved traffic management strategies to be developed based on the accurate traffic flow data obtained from the simulation.

Publication Date

4-19-2023

Project Designation

Independent Research

Primary Advisor

Van Nguyen

Primary Advisor's Department

Computer Science

Keywords

Stander Symposium, College of Arts and Sciences

Recommended Citation

"Vehicle Counting and Classification from Traffic Videos" (2023). Stander Symposium Projects. 3117.
https://ecommons.udayton.edu/stander_posters/3117