Committee Chair

Sartipi, Mina

Committee Member

Liang, Yu; Wu, Dalei

Department

Dept. of Computer Science and Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

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.

Degree

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.

Date

12-2023

Subject

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

Keyword

Deep learning; Objects detection; Objects tracking; Speed estimation; Camera calibration

Document Type

Masters theses

DCMI Type

Text

Extent

ix, 43 leaves

Language

English

Rights

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

License

http://creativecommons.org/licenses/by/4.0/

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