基于视频的交叉口排队过程感知及预测

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (1): 33-39.

• 智能交通系统与信息技术 • 上一篇下一篇

基于视频的交叉口排队过程感知及预测

余志^{* 1, 2}，黄柳红^{1, 2}，李熙莹^{1, 2}，栗波^{1, 2}，邹兵^{1, 2}

(1. 中山大学智能交通研究中心，广州 510006；2. 广东省智能交通系统重点实验室，广州 510006

收稿日期:2019-10-18 修回日期:2019-11-25 出版日期:2020-02-25 发布日期:2020-03-02
作者简介:余志(1961-)，男，江西九江人，教授.
基金资助:
国家自然科学基金/National Natural Science Foundation of China(U1611461).

Queueing Process Sensing and Prediction at Intersection Based on Video

YU Zhi^{1, 2}, HUANG Liu-hong^{1, 2}, LI Xi-ying^{1, 2}, LI Bo^{1, 2}, ZOU Bing^{1, 2}

1. Research Center of Intelligent Transport System, Sun Yat-Sen University, Guangzhou 510006, China; 2. Guangdong Provincial Key Laboratory of Intelligent Transport System, Guangzhou 510006, China

Received:2019-10-18 Revised:2019-11-25 Online:2020-02-25 Published:2020-03-02

摘要/Abstract

摘要：

在研究交叉口排队过程中，全面、精细地感知及预测方法一直是难点. 传统的排队过程感知方法缺乏不同排队阶段的精细感知和关联，无法得到全面、精细的交通参数. 对此，提出一种基于视频、分阶段感知再关联的排队过程感知及预测方法. 该方法包括3 部分：第一，通过目标检测和边缘检测提取道路和车辆信息；第二，对道路和车辆边缘信息进行卷积融合，检测排队区域和排队长度；第三，利用车辆位置重构和KCF跟踪等方法感知并关联不同排队阶段，重构并预测交叉口车辆运行轨迹，得到精准到车辆个体的排队车辆数和停车延误等参数. 实验结果表明，所提方法在检测正确的情况下，平均95.7%的排队车辆轨迹重构结果是准确的；并且在实际应用场景测试中，在不同车流量和光照条件下均可取得满意的效果.

关键词: 智能交通, 排队过程, 轨迹重构, 城市交叉口, 交通流参数

Abstract:

Queueing process consisting of queueing, accumulation, and disappearing phase plays a very important role for the analysis and optimization of traffic management and signal controls. However, most of existing methods are lack of detailed sensing and correlation between different queue stages, which leads to uncomprehensive and rough detection in traffic parameters. In this paper, we present a video- based method, by sensing in stages first and correlating later to sense and predict queueing process. Firstly, the detection algorithm of objects and edges are adopted to obtain information of road and vehicle. Secondly, fusing road information and vehicle edges to detect queueing area and queueing length. Finally, reconstructing vehicle trajectory by sensing and correlating different queue stages, which are divided by queueing area. On this basis, the various parameters are calculated with vehicle trajectory, and the experimental results show that the reconstruction accuracy reaches 95.7% when there are no detection errors, in addition, proposed method is robust in detecting various traffic parameters in practical applications.

Key words: intelligent transportation, queueing process, vehicle trajectory reconstruction, urban intersection, traffic parameters

中图分类号:

U491

余志，黄柳红，李熙莹，栗波，邹兵. 基于视频的交叉口排队过程感知及预测[J]. 交通运输系统工程与信息, 2020, 20(1): 33-39.

YU Zhi, HUANG Liu-hong, LI Xi-ying, LI Bo, ZOU Bing. Queueing Process Sensing and Prediction at Intersection Based on Video[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(1): 33-39.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19972.shtml

http://www.tseit.org.cn/CN/Y2020/V20/I1/33

参考文献

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基于视频的交叉口排队过程感知及预测

Queueing Process Sensing and Prediction at Intersection Based on Video

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