|Table of Contents|

Detection method of queuing vehicles on urban road(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2012年05期
Page:
100-109
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Detection method of queuing vehicles on urban road
Author(s):
SHI Zhong-ke;  QIAO Yu
School of Automation, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, Chin
Keywords:
traffic detection;  traffic parameter;  urban road;  texture feature;  ROC curve;  Gaussian mixture model;  frame difference method
PACS:
U495
DOI:
-
Abstract:
Aiming at the detection problem of queuing vehicles under the condition of urban road, a synthesis method based on edge information and local texture feature was put out. According to the characteristics of traffic enviroment, the performances of five different edge detection methods were compared, Canny algorithm was used to extract the edge information, and the improved LBP method was used to extract texture feature. The comprehensive detection result of vehicle was obtained, and the traffic parameters such as vehicle queuing length and lane occupancy rate were extracted. The proposed method, Gaussian mixture model and frame difference method were used to treat the video images of different scenes such as expressway, intersection, rainy weather, illumination mutation, heavy snowy weather and dense fog weather, and the quantitative evaluation of detection performance was carried out through ROC curve. Analysis result shows that under the scenes of expressway and heavy snowy weather, the detection performances of three methods are almost same, the best detection rates are close to 90.0% and 60.0% respectively, and false alarm rates are no more than 5.0% and 10.0% respectively. Under the scene of intersection, the best detection rates of three methods are 77.1%, 31.5% and 13.6% respectively, false alarm rates are 16.5%, 3.2% and 19.0% respectively. Under the scene of rainy weather, the best detection rates of three methods are 65.2%, 3.0% and 62.4%, false alarm rates are 10.5%, 5.0% and 56.5% respectively. Under the scene of illumination mutation, the best detection rates of three methods are 62.0%, 18.9% and 39.7%, false alarm rates are 10.8%, 55.1% and 36.0% respectively. Under the scene of dense fog weather, when visibility is lower, the detection rates and false alarm rates of three methods are close to zero. 25 figs, 22 refs.

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Last Update: 2012-11-05