基于VC++和MATLAB的车型分类及车辆计数系统
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摘要
随着社会经济发展,交通系统日益复杂,为了给交通系统的管理提供各种实时交通信息,以方便、高效的利用和管理现有的交通系统,我们研究开发了车型分类及车辆计数系统。
本系统的核心算法是基于汽车噪音信号,具体实现思路是:首先在线采集车辆声音信号,通过端点检测截取出有效部分,再对截取后的声音采用Brug法提取其AR模型参数,然后把参数送进训练好的BP神经网络进行车型分类并统计出每种车型的数量。整个系统采用VC++和MATLAB混合编程的方法实现。由于本系统的核心算法是由MATLAB实现,所以便于系统算法的更新。相对于传统的车型分类方法,本系统具有成本低、开发时间短、易于实现等优点。
本系统目前适用于单车道、小流量的公路,能够较准确的识别出公路上的基本车型。
With the development of society and economy, the traffic system became more and more complex. In the interest of providing the management of traffic system with real-time traffic information to achieve efficient management and make full use of the traffic system. We investigate a vehicle classification and counting system.
In this system we have designed a core algorithm based on vehicles noise, the concrete realization thought is: firstly, we on-line collect the vehicle noise, intercept the effective part of the noise by using endpoint detect method, and then extracting model parameter from AR spectrum analysis with Burg method, sending the parameter to the trained BP Neural network for classification, at last, calculating out the number of each vehicle model. The software system was constructed by VC++ and MATLAB. Because the core algorithm of the system was completed with MATLAB, it is facile to update the algorithm. Compared with the traditional vehicle classification method, this system has the advantages of shorter developing cycle, lower cost and easier to achieve.
At present, the system is suitable for single-lane and small flow of highway, can be more accurate to identify the basic models on the road.
本系统的核心算法是基于汽车噪音信号,具体实现思路是:首先在线采集车辆声音信号,通过端点检测截取出有效部分,再对截取后的声音采用Brug法提取其AR模型参数,然后把参数送进训练好的BP神经网络进行车型分类并统计出每种车型的数量。整个系统采用VC++和MATLAB混合编程的方法实现。由于本系统的核心算法是由MATLAB实现,所以便于系统算法的更新。相对于传统的车型分类方法,本系统具有成本低、开发时间短、易于实现等优点。
本系统目前适用于单车道、小流量的公路,能够较准确的识别出公路上的基本车型。
With the development of society and economy, the traffic system became more and more complex. In the interest of providing the management of traffic system with real-time traffic information to achieve efficient management and make full use of the traffic system. We investigate a vehicle classification and counting system.
In this system we have designed a core algorithm based on vehicles noise, the concrete realization thought is: firstly, we on-line collect the vehicle noise, intercept the effective part of the noise by using endpoint detect method, and then extracting model parameter from AR spectrum analysis with Burg method, sending the parameter to the trained BP Neural network for classification, at last, calculating out the number of each vehicle model. The software system was constructed by VC++ and MATLAB. Because the core algorithm of the system was completed with MATLAB, it is facile to update the algorithm. Compared with the traditional vehicle classification method, this system has the advantages of shorter developing cycle, lower cost and easier to achieve.
At present, the system is suitable for single-lane and small flow of highway, can be more accurate to identify the basic models on the road.
引文
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[2]刘玉新.常用车辆检测器性能比较与应用前景分析[J].公路交通科技(应用技术版),2007,(10).P26-30
[3] A.de la Escalera, J.Ma Armingol, M.Mata. Traffic sign recognition and analysis for intelligent vehicles. Image and Vision Computing,2003
[4]荆便顺.道路交通控制工程.北京:人民交通出版社,1995
[5] Sergios Theodoridi,Konstantinos Koutroumbas. Pattern Recognition(Third Edition).China Machine Press,2006
[6]边肇棋,张学工.模式识别(第二版)[M].北京:清华大学出版社,2000
[7]朱进,章新华.被动声呐目标识别技术的现状与展望[J].舰船科学技术, 2003,10.25(5):55-58
[8]明年汽车分类将采用新国家标准.北京市经济信息中心,2003
[9]中汽协启用汽车车行统计新分类标准[N].汽车周报.(1),2003
[10]收费公路车辆通行费车型分类新标准.交通部公路司,2003
[11] Roya Rad,Mansour Jamzad.Real time classification and tracking of multiple vehicles in highways.Pattern Recognition Letters,2005
[12]王双维,陈强,李江等.不同车型的车辆声音与振动信号特征研究[J].声学技术,2007.6.26(3):460-463
[13]陈强,李江,王双维等.基于AR模型的车型自动分类技术[J].吉林大学学报(工学版),2007.3.37(2):325-328
[14]国雁萌,盛任农,牟英良.基于能量和浊音特性的语音端点检测[J].计算机工程与应用,2006.(26):43-45
[15] Pickover C A, Khorasani A L. Fractal Characteristics of speech waveform graph[J]. Computer&Graphcs, 1986, 10(1):563-571.
[16]贾世楼.信息理论基础[M].哈尔滨哈尔滨工业大学出版社,1986
[17]王宝祥.信号与系统[M].哈尔滨工业大学出版社,2003
[18]李晔,崔慧娟,唐昆.基于能量和鉴别信息的语音端点检测算法[J].清华大学学报(自然科学版),2006.46(7):1271-1273,1277
[19]姚天任,孙洪.现代数字信号处理[M].武汉:华中科技大学出版社,1999
[20]赵丽华.输入空间自优化神经网络的研究及其在车型识别中的应用[D]:[硕士学位论文].长春:东北师范大学,2006
[21] Friedman M. and Kandel A.,Introduction to Pattern Recognition, statistical, structural, neural and fuzzy logic approaches.World Scientific,1999
[22] Wenming Cao , Feng Hao , Shoujue Wang.The application of DBF neural networks for object recognition.Information Sciences,2004
[23]杜丽萍.现代谱估计在噪声源识别中的应用[D]:[硕士学位论文].长春:东北师范大学,2007
[24]兰雪梅,朱健,黄承明等.BP网络的NLATLAB实现[J].微型电脑应用,2003,19(1):6-7.
[25]张志涌.精通MATLAB 6.5版[M].北京:北京航空航天大学出版社,2003.3.1.
[26] Neural Network Toolbox User's Guide[J].The MathWork,Inc,1995
[27]清汉计算机工作室.Visual C++6.0多媒体开发实例.机械工业出版社,2000
[28]李博轩编著.Visual C++6.0多媒体开发指南.清华大学出版社,2000
[29]刘祎玮.Visual C++视频/音频开发实用工程案例精选.人民邮电出版社,2004
[30]求是科技.Visual C++音视频编解码技术及实践.人民邮电出版社,2006
[31]苏金明.MATLAB与外部程序接口.电子工业出版社,2004
[32]刘维.精通Matlab与C/C++混合程序设计.北京航空航天大学出版社,2005
[33]宋晓宇.Visual C++高级编程技术与实例.中国水利水电出版社,2006
[34] MATLAB Help document[J].The Mathworks.Inc, 1999.