Liang, Yu; Wu, Dalei
College of Engineering and Computer Science
University of Tennessee at Chattanooga
Place of Publication
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.
M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.
Deep learning (Machine learning); Image processing--Digital techniques
ix, 43 leaves
Hassan, Yasir, "Deep learning-based framework for traffic estimation for the MLK Smart Corridor in downtown Chattanooga, TN" (2023). Masters Theses and Doctoral Dissertations.