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Observer Design for Discrete-Time Switched Lipschitz Nonlinear Singular Systems with Time Delays and Unknown Inputs Read chapter Read first chapter

2016 | OriginalPaper | Chapter

16. A Vision-Based Traffic Flow Detection Approach

Authors : Hongpeng Yin, Kun Zhang, Yi Chai

Published in: Proceedings of the 2015 Chinese Intelligent Systems Conference

Publisher: Springer Berlin Heidelberg

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Abstract

Traffic flow detection plays an important role in Intelligent Transportation System (ITS). However, the conventional traffic flow detection approaches are high cost or complex installation. In this paper, a reliably vision-based traffic flow detection approach is proposed. In this approach, Gaussian mixture model (GMM) is employed to model the dynamic background of traffic scene. Then, the binary foreground contours are extracted by image subtraction. Comparing the binary vehicle contours’ location and the current frame, the real and complete vehicles are obtained for detecting and monitoring. In the part of vehicle counting, to gather the vehicle flow parameter in each lane of the road and avoid the trouble of counting vehicles repeatedly, a discriminative method is presented to classify vehicles into different lanes. Experiment shows that a desired result can be achieved in the traffic flow detection system by the vision-based approach.

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previous chapter A Multi-objective Dual-Resource Shop Scheduling Model Considering the Differences Between Operational Efficiency
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Metadata
Title
A Vision-Based Traffic Flow Detection Approach
Authors
Hongpeng Yin
Kun Zhang
Yi Chai
Copyright Year
2016
Publisher
Springer Berlin Heidelberg
Book
Proceedings of the 2015 Chinese Intelligent Systems Conference

Print ISBN: 978-3-662-48384-8

Electronic ISBN: 978-3-662-48386-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-662-48386-2_16

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