Vision-based vehicle detection and tracking in intelligent transportation system
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Journal Title
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Insinööritieteiden korkeakoulu |
Master's thesis
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Authors
Date
2019-06-17
Department
Major/Subject
Mcode
Degree programme
Master's Programme in Mechanical Engineering (MEC)
Language
en
Pages
64+13
Series
Abstract
This thesis aims to realize vision-based vehicle detection and tracking in the Intelligent Transportation System. First, it introduces the methods for vehicle detection and tracking. Next, it establishes the sensor fusion framework of the system, including dynamic model and sensor model. Then, it simulates the traffic scene at a crossroad by a driving simulator, where the research target is one single car, and the traffic scene is ideal. YOLO Neural Network is applied to the image sequence for vehicle detection. Kalman filter method, extended Kalman filter method, and particle filter method are utilized and compared for vehicle tracking. The Following part is the practical experiment where there are multiple vehicles at the same time, and the traffic scene is in real life with various interference factors. YOLO Neural Network combined with OpenCV is adopted to realize real-time vehicle detection. Kalman filter and extended Kalman filter are applied for vehicle tracking; an identification algorithm is proposed to solve the occlusion of the vehicles. The effects of process noise as well as measurement noise are analysed using variable-controlling approach. Additionally, perspective transformation is illustrated and implemented to transfer the coordinates from the image plane to the ground plane. If the vision-based vehicle detection and tracking can be realized and popularized in daily lives, vehicle information can be shared among infrastructures, vehicles, and users, so as to build interactions inside the Intelligent Transportation System.Description
Supervisor
Tammi, KariThesis advisor
Tammi, KariKeywords
intelligent transportation system, computer vision, vehicle detection, vehicle tracking, YOLO neural network, sensor fusion