The Spatial Representation of the Computer Vision Based Motorcycle Relative Distance
Date Issued
2015
Date
2015
Author(s)
Hsieh, Meng-Hsiu
Abstract
Individual motorcycle behavior depends on the surrounding environment such as traffic flow and the geometric design of the road. For their own safety, motorcyclists react to the distance and velocity of other vehicles. Especially during rush hours, the traffic condition changes rapidly. By setting up a camera at the road intersection to record traffic flow videos, the relative spatial position and the motorcycles’ direction angle are measured by analyzing the data. The motorcycle behavior at the intersection is observed and analyzed through computer vision and image processing methods. The Histogram of Gradients (HOG) descriptor is adapted for the detection of motorcycles utilizing a Support Vector Machine (SVM), and the Kalman Filter is employed for the tracking of the motorcycles’ trace. Through this approach, we observe the motorcycles’ traffic flow and traffic characteristics such as: (1) motorcycle trajectories on specific road section, (2) motorcycle traffic volume and average speed, (3) the pattern of accumulated relative spatial position of all the motorcycles from the video along the horizontal axis and vertical axis, and (4) the pattern of accumulated relative spatial positions of relatively fast and relatively slow motorcycles from the video. We have taken real world videos for the validation of the proposed approach. The results of this study could serve as a reference for traffic safety guidance for motorcyclists and could potentially be applied for detection of danger road geometries.
Subjects
Computer Vision
Histogram of Oriented Gradient
Support Vector Machine
Kalman Filter
Motorcycle
Traffic Safety
Type
thesis
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