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Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning./
作者:
Ward, Tyler.
面頁冊數:
1 online resource (76 pages)
附註:
Source: Masters Abstracts International, Volume: 84-11.
Contained By:
Masters Abstracts International84-11.
標題:
Computer engineering. -
電子資源:
click for full text (PQDT)
ISBN:
9798379443221
Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning.
Ward, Tyler.
Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning.
- 1 online resource (76 pages)
Source: Masters Abstracts International, Volume: 84-11.
Thesis (M.S.)--Morehead State University, 2023.
Includes bibliographical references
Human activity recognition and prediction systems are crucial to the safety of autonomous vehicles. While much research has been conducted to improve these systems, very little has been done to address the important task of differentiating between adult and child pedestrians. Failure to correctly identify the type of pedestrian can lead to accidents. In this thesis, a novel multiple object tracking system for autonomous vehicles is proposed that overcomes the challenges of differentiating between adult and child pedestrians. To increase the system's robustness, it is also capable of identifying and tracking 51 different animal types that are commonly encountered on roads around the world. The proposed system uses modern machine learning methods for object detection and tracking to identify the type of pedestrian or animal, and also measure various characteristics of their behavior, such as speed and trajectory. Experimental results indicate effectiveness in accomplishing these tasks, demonstrating the potential of the multiple object tracking system to improve the safety and performance of autonomous vehicles.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024
Mode of access: World Wide Web
ISBN: 9798379443221Subjects--Topical Terms:
569006
Computer engineering.
Subjects--Index Terms:
Autonomous vehiclesIndex Terms--Genre/Form:
554714
Electronic books.
Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning.
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Source: Masters Abstracts International, Volume: 84-11.
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Advisor: Rashad, Sherif.
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Thesis (M.S.)--Morehead State University, 2023.
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Includes bibliographical references
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Human activity recognition and prediction systems are crucial to the safety of autonomous vehicles. While much research has been conducted to improve these systems, very little has been done to address the important task of differentiating between adult and child pedestrians. Failure to correctly identify the type of pedestrian can lead to accidents. In this thesis, a novel multiple object tracking system for autonomous vehicles is proposed that overcomes the challenges of differentiating between adult and child pedestrians. To increase the system's robustness, it is also capable of identifying and tracking 51 different animal types that are commonly encountered on roads around the world. The proposed system uses modern machine learning methods for object detection and tracking to identify the type of pedestrian or animal, and also measure various characteristics of their behavior, such as speed and trajectory. Experimental results indicate effectiveness in accomplishing these tasks, demonstrating the potential of the multiple object tracking system to improve the safety and performance of autonomous vehicles.
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