计算机科学 ›› 2018, Vol. 45 ›› Issue (6): 314-319.doi: 10.11896/j.issn.1002-137X.2018.06.055

• 图形图像与模式识别 • 上一篇    

基于时空关系模型的交通信号灯的实时检测与识别

李宗鑫, 秦勃, 王梦倩   

  1. 中国海洋大学计算机科学与技术系 山东 青岛266100
  • 收稿日期:2016-12-18 出版日期:2018-06-15 发布日期:2018-07-24
  • 作者简介:李宗鑫(1989-),男,硕士生,主要研究方向为计算机图形图像处理、并行计算,E-mail:ningyanglzx@163.com;秦 勃(1964-),男,博士,教授,主要研究方向为计算机图形图像处理、并行计算、云计算,E-mail:qinbo@ouc.edu.cn(通信作者);王梦倩(1993-),女,硕士生,主要研究方向为计算机图形图像处理、并行计算
  • 基金资助:
    本文受国家自然科学基金(61102108),湖南省自然科学基金(2016JJ3106),湖南省教育厅项目(16B225,YB2013B039),南华大学青年英才支持计划和南华大学重点学科(NHXK04)资助

Real-time Detection and Recognition of Traffic Light Based on Time-Space Model

LI Zong-xin, QIN Bo, WANG Meng-qian   

  1. Department of Computer Science & Technology,Ocean University of China,Qingdao,Shandong 266100,China
  • Received:2016-12-18 Online:2018-06-15 Published:2018-07-24

摘要: 交通信号灯的检测与识别是无人驾驶汽车和高级驾驶辅助系统(ADAS)的重要组成部分。针对城市道路复杂环境下的交通信号灯的检测和识别需求,依据多帧视频图像序列的时空连续变化关系构建多帧视频图像的时空关系模型(Time-Space Model,TSM),提出了一种新的基于多帧视频图像序列的交通信号灯的检测和识别算法。算法包含3部分:基于颜色的视频图像快速分割压缩算法,用于提高计算效率;引入多帧视频图像序列的时空关系模型,以提高交通信号灯检测的准确性;根据图像的HOG(Histogram of Oriented Gradient)特征,通过SVM(Support Vector Machine)分类器对信号灯进行识别。实验结果表明,算法的鲁棒性强、检测识别速度快、准确率高。

关键词: ADAS, 交通信号灯检测, 模式识别, 时空关系模型, 图像快速分割

Abstract: Detection and recognition of traffic light are important for driverless cars and advanced driver assistance systems(ADAS).In order to satisfy the requirements of traffic light detection and recognition in complex urban environment,a real-time detection and recognition algorithm based on time-space model (TSM) was proposed.It was established based on thetime-space continuous variation relationship of video-frame sequence.The proposed algorithm consists of three parts.The first part is fast image segmentation and compression algorithm based on color,which is used to improve the computational efficiency.Second,time-space model of multi-frame image sequence is introduced to improve the accuracy of detection stage.Third,recognition of traffic lights is achieved by using support vector machine (SVM) with histogram of oriented gradients (HOG) features.Experiment results show that this novel algorithm has strong robustness,high efficiency and accuracy.

Key words: ADAS, Fast image segmentation, Pattern recognition, Time-space model, Traffic light detection

中图分类号: 

  • TP391
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