Deep learning-based framework for traffic estimation for the MLK Smart Corridor in downtown Chattanooga, TN

In this Thesis we introduced a deep learning-based framework for vehicles detection, tracking, movement direction identification, and speed estimation. We chose YOLOv7 for objects detection given its ability to run up to 160 fps. We trained YOLOv7 to detect and classify vehicles into four classes with a reported mean average precision of 0.69. For re-identification, we refined the DeepSort tracker, a tracking-by-detection model. We incorporated a Siamese network in place of its default feature extractor. Both models were trained on the UA-DETRAC dataset, tested on KITTI, revealing a 71\% reduction in the IDSW rate with our revision. Movement direction classification, an offline system component, utilized a similarity-based trajectory method with specific spatial constraints. Finally we combined image perspective transformation with objects scaling to estimate speed with an error of 0.516 mph. Our comprehensive framework offers potential in applications like travel time estimation and benchmarking speed data.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Hassan, Yasir

Contributors dc:contributor

• Sartipi, Mina
• Liang, Yu; Wu, Dalei
• College of Engineering and Computer Science

• Deep learning (Machine learning)
• Image processing--Digital techniques

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/841

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-2016