利用FCM对静态图像进行交通状态识别

doi:10.3969/j.issn.1001-2400.2017.06.015

西安电子科技大学学报

利用FCM对静态图像进行交通状态识别

崔华;袁超;魏泽发;李盼侬;宋鑫鑫;纪宇;刘云飞

(长安大学信息工程学院，陕西西安 710064)

收稿日期:2016-12-12 出版日期:2017-12-20 发布日期:2018-01-18
作者简介:崔华(1977-)，女，教授，博士，E-mail: huacui@chd.edu.cn
基金资助:
国家自然科学基金资助项目(61572083)；陕西省自然科学基金资助项目(2015JQ6230)；中央高校基本科研业务费专项资金资助项目(310824152009)

Traffic state recognition using static images and FCM

CUI Hua;YUAN Chao;WEI Zefa;LI Pannong;SONG Xinxin;JI Yu;LIU Yunfei

(School of Information Engineering, Chang'an Univ., Xi'an, China)

Received:2016-12-12 Online:2017-12-20 Published:2018-01-18

摘要/Abstract

摘要：

对交通状态进行准确识别可以主动预警将要进入本路段的驾驶员避开拥堵，以免加重拥堵程度，同时也是科学制定主动交通管理决策的基础，有利于及时疏导拥堵，提高道路运行效率，节能减排．首先从交通监控视频中采集图像，标注道路为兴趣区，并对道路图像做角度和尺度的归一化处理；然后提取兴趣区图像的平均梯度、角点个数和长边缘比例3个特征；最后，利用模糊C均值聚类算法将图片所呈现的交通状态分为畅通和拥堵两种状态．实验结果表明，文中算法可以有效识别图像中的交通状态，正确率达到了94％以上，而且较基于视频的交通状态识别方法，该方法也大大降低了实现成本．

关键词: 交通状态识别, 交通图像, 模糊C均值聚类, 角点个数, 长边缘比例

Abstract:

Accurate recognition of the traffic condition can proactively alert drivers who will enter the congested road to avoid congestion, so that the degree of congestion will not be increased. And it is also the basis to make scientific decision on active traffic managements, and conducive to alleviate congestion, improve the traffic efficiency, save energy and reduce emission. In this paper, the traffic surveillance videos are sampled every three minutes to build static image database, and the road area is marked as the region of interest (ROI), and then ROI images are normalized in terms of angle and scale. The three image features in ROI, i.e., average gradient, corner and long edge number, are then extracted. Finally, the fuzzy C-means clustering (FCM) method is used to classify the traffic condition into two classifications, i.e., flowing traffic and congestion. Experimental results show that the proposed algorithm can effectively identify the traffic condition involved in the image by the accuracy of 98％. Moreover, compared with the video-based approaches, this method greatly reduces the implementation cost.

Key words: traffic condition recognition, traffic image, fuzzy C-means clustering, corner, long edge number

崔华;袁超;魏泽发;李盼侬;宋鑫鑫;纪宇;刘云飞. 利用FCM对静态图像进行交通状态识别[J]. 西安电子科技大学学报, 2017, 44(6): 79-84.

CUI Hua;YUAN Chao;WEI Zefa;LI Pannong;SONG Xinxin;JI Yu;LIU Yunfei. Traffic state recognition using static images and FCM[J]. Journal of Xidian University, 2017, 44(6): 79-84.

参考文献

［1］ ZHU H M, YU F Q. A Cross-correlation Technique for Vehicle Detections in Wireless Magnetic Sensor Network［J］. IEEE Sensors Journal, 2016, 16(11): 4484-4494.
［2］ TYAGI V, KALYANARAMAN S, KRISHNAPURAM R. Vehicular Traffic Density State Estimation Based on Cumulative Road Acoustics［J］. IEEE Transactions on Intelligent Transportation Systems, 2012(13): 1156-1166.
［3］ ZHANG P, LUO Q. ZHOU S Y. Adaptive Background Updating Algorithm for Traffic Congestion Detection Based on Kalman Filtering and Inter-frame Centroid Distance［C］//Proceedings of the 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference. Piscataway: IEEE, 2015: 891-895.
［4］ XU X J, GAO X B, ZHAO X W, et al. A Novel Algorithm for Urban Traffic Congestion Detectionbased on GPS Data Compression［C］//Proceedings of the 2016 IEEE International Conference on Service Operations and Logistics, and Informatics. Piscataway: IEEE, 2016: 107-112.
［5］ YOUNES M B, BOUKERCHE A. Efficient Traffic Congestion Detection Protocol for Next Generation VANETs［C］//Proceedings of the IEEE International Conference on Communications. Piscataway: IEEE, 2013: 3764-3768.
［6］ ZHANG P, RUI L L, QIU X S, et al. A New Fusion Structure Model for Real-time Urban Trafficstate Estimation by Multisource Traffic Data Fusion［C］//Proceedings of the 2016 Asia-Pacific Network Operations and Management Symposium. Piscataway: IEEE, 2016: 1-6.
［7］ HASAN M M, SAHA G, HOQUE A, et al. Smart Traffic Control System with Application of Image Processing Techniques［C］//Proceedings of the 2014 International Conference on Informatics, Electronics and Vision. Piscataway: IEEE, 2014: 6850751.
［8］ ZHOU L, JI L, HAO D. Traffic Congestion Judgment Based on Linear Spatial Pyramid Matching Using Sparse Coding［C］//2014 10th International Conference on Natural Computation. Piscataway: IEEE, 2014: 540-544.
［9］王天. 基于纹理分析的虚拟结肠镜CAD技术研究［D］. 西安: 西安电子科技大学, 2008.
［10］ MAHMOUDZADEH E, ZEKRI M, MONTAZERI M A, et al. Directional SUSAN Image Boundary Detection of Breast Thermogram［J］. IET Image Processing, 2016, 10(7): 552-560.
［11］章为川, 水鹏朗, 徐国靖. 边缘线上各向异性高斯核信息熵的角点检测［J］. 西安电子科技大学学报, 2013, 40(4): 119 -124.
ZHANG Weichuan, SHUI Penglang, XU Guojing. Corner Detection via Anisotropic Gaussian Kernels Information Entropy on Edge Contours［J］. Journal of Xidian University, 2013, 40(4): 119-124.
［12］陈东辉. 基于目标函数的模糊聚类算法关键技术研究［D］. 西安: 西安电子科技大学, 2012.
［13］ DINANI M A, AHMADI P, GHOLAMPOUR I. Efficient Feature Extraction for Highway Traffic Density Classification［C］//Proceedings of the 2015 9th Iranian Conference on Machine Vision and Image Processing . Piscataway: IEEE, 2015: 14-19.

利用FCM对静态图像进行交通状态识别

Traffic state recognition using static images and FCM

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

Metrics

本文评价

推荐阅读 10