|Table of Contents|

Traffic incident acoustic recognition method based on wavelet decomposition and support vector machine(PDF)

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

Issue:
2010年02期
Page:
116-121
Research Field:
交通信息工程及控制
Publishing date:
2010-04-20

Info

Title:
Traffic incident acoustic recognition method based on wavelet decomposition and support vector machine
Author(s):
LUO Xiang-long1; 2;  GAO Jing-huai2;  NIU Guo-hong3;  PAN Ruo-yu4
1. School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; 2. School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China; 3. Xi'an Municipal Engineering Design and Research Institute Co., Ltd., Xi'an 710068, Shaanxi, China; 4. School of Communication and information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710064, Shaanxi, China
Keywords:
traffic information processing;  traffic incident;  wavelet decomposition;  SVM
PACS:
U491.116
DOI:
-
Abstract:
The existing automatic detection and recognition methods of traffic incidents were analyzed, a recognition method with vehicle acoustic signals was proposed based on wavelet decomposition(WD)and support vector machine(SVM). Vehicle acoustic signals were decomposed with WD, the powers in different frequencies were regarded as different incident eigenvectors, and the traffic incident classifier composed of multiple SVMs was trained. The acoustic signals of normal driving, braking and crash incidents were recognized. Test result shows that various traffic incidents can be recognized with vehicle acoustic signals, the recognition rate reaches 95%, so the proposed method is feasible. 1 tab, 3 figs, 16 refs.

References:

[1] 庄 斌,杨晓光,李克平.道路交通拥挤事件判别准则与检测算法[J].中国公路学报,2006,19(3):82-86. ZHUANG Bin, YANG Xiao-guang, LI Ke-ping. Criterion and detection algorithm for road traffic congestion incidents[J]. China Journal of Highway and Transport, 2006, 19(3): 82-86.(in Chinese)
[2] 何 杰,胡如夫,李传志,等.基于无线定位终端的公路事件检测方法研究[J].系统仿真学报,2009,21(12):3828-3832. HE Jie, HU Ru-fu, LI Chuan-zhi, et al. Study on freeway incident detection using integrated wireless position terminal[J]. Journal of System Simulation, 2009, 21(12): 3828-3832.(in Chinese)
[3] 汪 勤,黄 山,张洪斌,等.基于视频图像处理的交通事件检测系统[J].计算机应用,2008,28(7):1886-1889. WANG Qin, HUANG Shan, ZHANG Hong-bin, et al. Traffic incident detection system based on video image processing[J]. Computer Applications, 2008, 28(7): 1886-1889.(in Chinese)
[4] 张存保,杨晓光,严新平.基于浮动车的高速公路交通事件自动判别方法研究[J].武汉理工大学学报:交通科学与工程版,2006,30(6):973-975,983. ZHANG Cun-bao, YANG Xiao-guang, YAN Xin-ping. An automatic incident detection methodology for freeway using floating cars[J]. Journal of Wuhan University of Technology: Transportation Science and Engineering, 2006, 30(6): 973-975, 983.(in Chinese)
[5] 郭艳玲,吴义虎,黄中祥.基于小波分析和SOM网络的交通事件检测算法[J].系统工程,2006,24(10):100-104. GUO Yan-ling, WU Yi-hu, HUANG Zhong-xiang. An algorithm for traffic incidents detection based on wavelet analysis and SOM network[J]. Systems Engineering, 2006, 24(10): 100-104.(in Chinese)
[6] 陈 斌.基于支持向量机的高速公路意外事件检测模型[J].中国公路学报,2006,19(6):107-112.CHEN Bin. Freeway accident detection model based on support vector machine[J]. China Journal of Highway and Transport, 2006, 19(6): 107-112.(in Chinese)
[7] 张敬磊,王晓原.交通事件检测算法研究进展[J].武汉理工大学学报:交通科学与工程版,2005,29(2):215-218. ZHANG Jing-lei, WANG Xiao-yuan. Research progress of traffic incident automatic detection algorithms[J]. Journal of Wuhan University of Technology: Transportation Science and Engineering, 2005, 29(2): 215-218.(in Chinese)
[8] WANG Wei, CHEN Shu-yan, QU Gao-feng. Incident detection algorithm based on partial least squares regression[J]. Transportation Research Part C: Emerging Technologies, 2008, 16(1): 54-70.
[9] CHEN S, SUN Z P, BRIDGE B. Automatic traffic monitor- ing by intelligent sound detection[C]∥ITSC.Proceedings of IEEE Intelligent Transportation Systems Conference. Boston: IEEE, 1997: 171-176.
[10] BALRAJ N. Automated accident detection in intersections via digital audio signal processing [D]. Starkville: Mississippi State University, 2003.
[11] 熊烈强,商 蕾,高孝洪.基于噪声和振动的快速路交通事件检测方法[J].武汉理工大学学报:交通科学与工程版,2005,29(2):238-241. XIONG Lie-qiang, SHANG Lei, GAO Xiao-hong. A study of AID for urban expressway based on traffic noise and road vibration[J]. Journal of Wuhan University of Technology: Transportation Science and Engineering, 2005, 29(2): 238-241.(in Chinese)
[12] 陈 强.高速公路交通流特征参数被动声学检测技术研究[D].长春:吉林大学,2005. CHEN Qiang. Research on the technology of passive acoustics detection of expressway traffic flow characteristic parameters[D]. Changchun: Jilin University, 2005.(in Chinese)
[13] MALLAT S G. A theory for multiresolution signal decompos- ition: the wavelet representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693.
[14] MILLET-ROIG J,VENTURA-GALIANO R, CHORRO-GASCO F J, et al. Support vector machine for arrhythmia discrimination with wavelet transform-based feature selection[J]. Computers in Cardiology, 2000, 27(1): 407-410.
[15] HSU C W, LIN C J. A comparison of methods for multiclass support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425.
[16] OLIVIER C, VLADIMIR V, OLIVIER B, et al. Choosing multiple parameters for support vector machines[J]. Machine Learning, 2002, 46(1): 131-159.

Memo

Memo:
-
Last Update: 2010-04-20