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The Ohio State University

1. Stoddart, Evan. Computer Vision Techniques for Automotive Perception Systems.

Degree: MS, Electrical and Computer Engineering, 2019, The Ohio State University

Computer vision is widely used in modern robotics and automated machinery due to its highly diverse ability to recognize patterns, track objects, and map environments. Over the last decade, computer vision has also become an integral part in advanced driver assistance systems (ADAS) which assist drivers in operating motor vehicles. Modern ADAS use sensors, such as cameras, to characterize the environment and either alert the driver or take control to lessen the severity of, and even prevent, accidents. This work discusses a vision-based, SAE Level 0 ADAS system designed for a collegiate automotive design competition. The experimental design and setup will introduce the custom, hybrid-electric ego-vehicle, used as a prototype deployment vehicle for the system, as well as the components and software practices. Next, the computer vision techniques used to detect and track target vehicles, will be discussed. This is structured chronologically in the stages followed to make a camera-based system. This included component modeling and calibration, image processing and computer vision operations, and application-focused feature extraction techniques used in lane line identification, vehicle classification and localization, and vehicle tracking. All software developed for this system used the OpenCV library for Python and C++. This library is easy to use and well-documented, making it a convenient resource for a plethora of vision-based algorithms. Validation techniques will also be discussed in terms of software-in-the-loop, hardware-in-the-loop, and in-vehicle testing. The system placed 4th of 16 schools in the ADAS Evaluation Event at the end of year competition. The performance of the system is discussed. The performance results were sufficient for the needs of the competition; however, additional reliability would be desired for commercial use. The concepts discussed in this work, however, show promise towards implementing a similar system by following the systems-level development and validation practices discussed. This work serves as a template for the design and implementation of a vision-based perception system with relevance to not only driver assistance, but autonomous vehicles and robotics as well. Advisors/Committee Members: Midlam-Mohler, Shawn (Advisor).

Subjects/Keywords: Computer Engineering; Computer Science; Automotive Engineering; Engineering; ADAS; computer vision; machine learning

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APA (6th Edition):

Stoddart, E. (2019). Computer Vision Techniques for Automotive Perception Systems. (Masters Thesis). The Ohio State University. Retrieved from

Chicago Manual of Style (16th Edition):

Stoddart, Evan. “Computer Vision Techniques for Automotive Perception Systems.” 2019. Masters Thesis, The Ohio State University. Accessed September 19, 2019.

MLA Handbook (7th Edition):

Stoddart, Evan. “Computer Vision Techniques for Automotive Perception Systems.” 2019. Web. 19 Sep 2019.


Stoddart E. Computer Vision Techniques for Automotive Perception Systems. [Internet] [Masters thesis]. The Ohio State University; 2019. [cited 2019 Sep 19]. Available from:

Council of Science Editors:

Stoddart E. Computer Vision Techniques for Automotive Perception Systems. [Masters Thesis]. The Ohio State University; 2019. Available from: