基于分布式视频网络的交叉口车辆精确定位方法

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清华大学辅仁网/2017-07-07

杨德亮^1,2, 谢旭东¹, 李春文¹, 牛小铁²

1. 清华大学自动化系, 北京 100084;
2. 北京工业职业技术学院机电工程系, 北京 100042

Accurate vehicle location method at an intersection based on distributed video networks

YANG Deliang^1,2, XIE Xudong¹, Li Chunwen¹, NIU Xiaotie²

1. Department of Automation, Tsinghua University, Beijing 100084, China;
2. Department of Mechanical and Electrical Engineering, Beijing Polytechnic College, Beijing 100042, China

摘要:

输出: BibTeX | EndNote (RIS)

摘要为了对交叉口车辆的位置进行准确定位, 提出了一种分布式视频网络架构下车辆精确定位方法。在分布式视频网络中每处摄像机架设位置均设有2类摄像机: 近景摄像机和远景摄像机。首先在近景摄像机拍摄范围内, 对感兴趣区域内车辆进行身份识别, 根据车牌照平面与道路平面垂直的约束条件, 建立车牌照模型来对车辆精确定位; 接着在远景摄像机拍摄范围内, 采用融合局部二值模式(LBP)纹理特征的金字塔稀疏光流法实时跟踪车辆上局部特征点, 根据特征点运动趋势相似性获得稳态特征点, 来对车辆位置估计; 最后根据不同摄像机检测结果, 采用加权一致性信息融合算法来提高车辆定位精度。实验结果表明: 该方法能对交叉口车辆位置进行精确定位。

关键词 ：车辆精确定位,分布式视频网络,加权一致性信息融合,车牌照模型

Abstract：A robust framework is given for precise vehicle localization in intersections using distributed video networks. Each intersection is equipped with short-range and long-range cameras in a distributed video network. If the vehicle is in the shooting range of the short-range camera, within the region of interest for vehicle identification, and the license plate is perpendicular to the road plane, a vehicle license plate model is used to accurately locate the vehicle position. If the vehicle is in the shooting range of the long-range camera, a pyramid sparse optical flow algorithm with LBP texture features is used in real-time to track the local feature points on the vehicle to estimate the vehicle position based on stable feature points obtained from the similar motions. Finally, information is exchanged between the cameras, a weighted consensus information fusion algorithm is used to obtain a globally optimal estimate of the vehicle position. Tests show that this method can accurately locate the vehicle position at intersections.

Key words：precise vehicle location distributed video networks weighted consensus information fusion vehicle license plate model

收稿日期: 2015-11-02 出版日期: 2016-04-01

ZTFLH:

TN911.73

通讯作者:谢旭东,副教授,E-mail:xdxie@tsinghua.edu.cnE-mail: xdxie@tsinghua.edu.cn

引用本文:

杨德亮, 谢旭东, 李春文, 牛小铁. 基于分布式视频网络的交叉口车辆精确定位方法[J]. 清华大学学报（自然科学版）, 2016, 56(3): 281-286,293.
YANG Deliang, XIE Xudong, Li Chunwen, NIU Xiaotie. Accurate vehicle location method at an intersection based on distributed video networks. Journal of Tsinghua University(Science and Technology), 2016, 56(3): 281-286,293.

链接本文:

http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2016.21.033或 http://jst.tsinghuajournals.com/CN/Y2016/V56/I3/281

图表:

图1　道路上方网络摄像机固定架设示意图

图2　摄像机架设图和2类摄像机拍摄的图像

图3　系统处理流程图

图4　车牌照尺寸和模型

图5　跟踪特征点对车辆位置估计

图6　摄像机i中加权一致性信息融合的车辆位置跟踪流程图

图7　实验场所

图8　某时间段车辆定位结果对比图

表1　车辆定位结果的平均误差和有效范围对比表

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