DocumentCode :
20463
Title :
Vehicle trajectory extraction by simple two-dimensional model matching at low camera angles in intersection
Author :
Guangquan Lu ; Longfei Kong ; Yunpeng Wang ; Daxin Tian
Author_Institution :
Beijing Key Lab. for Cooperative Vehicle Infrastruct. Syst. & Safety Control, Beihang Univ., Beijing, China
Volume :
8
Issue :
7
fYear :
2014
fDate :
11 2014
Firstpage :
631
Lastpage :
638
Abstract :
Vehicle trajectories are commonly used to analyse driving behaviour and traffic safety. Traditional blob detection methods are limited in providing the accurate locations of vehicles when these are monitored from a low position. The authors propose a novel method of vehicle detection by utilising the projection line between the vehicle side and the ground plane. The line, which is extremely close to the surface of the road plane, provides location and orientation information. Therefore the authors´ method further locates vehicles by matching the two-dimensional (2D) model with the vehicle bottom by using a projection line and a projective transformation matrix. The model-based tracking method is used to track the detected vehicles, and a Kalman filter is combined to predict the locations of vehicles. The output is a set of microscopic parameters which include vehicle ID, trajectory, velocity, acceleration, orientation and angular speed, respectively. The experimental results are acceptable in terms of vehicle detection, vehicle tracking, trajectory extraction and computation time, respectively.
Keywords :
Kalman filters; cameras; feature extraction; object detection; object tracking; road safety; road traffic; road vehicles; 2D model; Kalman filter; angular speed; at low camera angles; blob detection methods; detected vehicle tracking; driving behaviour analysis; ground plane; microscopic parameters; model-based tracking method; orientation information; projection line; road plane; traffic safety; two-dimensional model matching; vehicle ID; vehicle detection method; vehicle side; vehicle trajectory extraction;
fLanguage :
English
Journal_Title :
Intelligent Transport Systems, IET
Publisher :
iet
ISSN :
1751-956X
Type :
jour
DOI :
10.1049/iet-its.2013.0151
Filename :
6940419
Link To Document :
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