复杂场景下声频传感器网络核稀疏表示车辆识别

doi:10.3969/j.issn.1001-2400.2015.04.019

J4 ›› 2015, Vol. 42 ›› Issue (4): 114-120.doi: 10.3969/j.issn.1001-2400.2015.04.019

复杂场景下声频传感器网络核稀疏表示车辆识别

王瑞1;王康晏1;冯玉田1;张海燕1;金彦亮1;张有正2

(1. 上海大学通信与信息工程学院，上海 200444；
2. 衢州职业技术学院，浙江衢州 324000)

收稿日期:2014-04-09 出版日期:2015-08-20 发布日期:2015-10-12
通讯作者: 王瑞
作者简介:王瑞(1982-)，男，副教授，E-mail: rwang@shu.edu.cn．
基金资助:
国家自然科学基金资助项目(61301027, 61375015, 11274226)；浙江省自然科学基金资助项目(LY14F030007)

Vehicle recognition using acoustic sensor networks in complex scenes via kernel sparse representation

WANG Rui1;WANG Kangyan1;FENG Yutian1;ZHANG Haiyan1;JIN Yanliang1;ZHANG Youzheng2

(1. School of Communication and Information Engineering, Shanghai Univ., Shanghai 200444, China;
2. Quzhou College of Technology, Zhejiang 324000, China)

Received:2014-04-09 Online:2015-08-20 Published:2015-10-12
Contact: WANG Rui

摘要/Abstract

摘要：

提出一种基于核稀疏表示的声频传感器网络车辆识别方法．首先利用Mel频率倒谱系数提取车辆声音特征；然后采用核方法将其投影到高维特征空间以实现线性可分，将线性稀疏表示扩展到核域空间，构建过完备字典，求解核稀疏最优化问题对目标车辆进行分类．实验验证了该算法在声频数据集结构复杂的情况下，能有效地识别目标车辆，与传统的声频目标分类算法相比，提高了识别的准确率．

关键词: 核稀疏表示, Mel频率倒谱系数, 车辆识别, 复杂场景, 传感器网络

Abstract:

This paper proposes a method of vehicle recognition via kernel sparse representation using acoustic sensor networks in complex scenes. This algorithm uses the Mel frequency cepstral coefficients to extract the acoustic features of vehicles and maps them into a high-dimensional feature space with a kernel function to get linearly separable samples. After extending sparse representation to the kernel space and constructing the over-complete dictionary, the objective vehicles will be recognized by solving the optimization problem. Experiments show that the proposed algorithm gives good performance on vehicle recognition under the circumstance of complex data sets. Compared with other traditional acoustic classification algorithms, the method improves the precision of recognition.

Key words: kernel sparse representation, Mel frequency cepstral coefficients, vehicle recognition, complex scenes, sensor networks

王瑞;王康晏;冯玉田;张海燕;金彦亮;张有正. 复杂场景下声频传感器网络核稀疏表示车辆识别[J]. J4, 2015, 42(4): 114-120.

WANG Rui;WANG Kangyan;FENG Yutian;ZHANG Haiyan;JIN Yanliang;ZHANG Youzheng. Vehicle recognition using acoustic sensor networks in complex scenes via kernel sparse representation[J]. J4, 2015, 42(4): 114-120.

参考文献

［1］肖汉光, 蔡从中, 廖克俊. 利用声波和地震波识别军事车辆类型［J］. 系统工程理论与实践, 2006, 26(4): 108-113.
Xiao Hanguang, Cai Congzhong, Liao Kejun. Recognition of Military Vehicles by Using Acoustic and Seismic Signals ［J］. System Engineering-Theory ＆ Practice, 2006, 26(4): 108-113.
［2］刘桂林, 孔祥维, 刘航. 基于无线传感器网络的车辆检测识别算法研究［J］. 传感器与微系统, 2010(2): 9-12.
Liu Guilin, Kong Xiangwei, Liu Hang. Research on Vehicle Detection and Classification Algorithm Based on Wireless Sensor Networks ［J］. Transducer and Microsystem Technologies, 2010(2): 9-12.
［3］ Li D, Wong K D, Hu Y H, et al. Detection, Classification and Tracking of Targets in Distributed Sensor Networks ［J］. IEEE Signal Processing Magazine, 2002, 19: 11-29.
［4］ Duarte M F, Hu Y H. Vehicle Classification in Distributed Sensor Networks ［J］. Journal of Parallel and Distributed Computing, 2004, 64(7): 826-838.
［5］ Ghasemzadeh H, Jafari R. Physical Movement Monitoring Using Body Sensor Networks: a Phonological Approach to Construct Spatial Decision Trees ［J］. IEEE Transactions on Industrial Informatics, 2011, 7(1): 66-77.
［6］ Wang Z L, Jiang M, Hu Y H, et al. An Incremental Learning Method Based on Probabilistic Neural Networks and Adjustable Fuzzy Clustering for Human Activity Recognition by Using Wearable Sensors ［J］. IEEE Transactions on Information Technology in Biomedicine, 2012, 16(4): 691-699.
［7］ Wright J, Yang A Y, Ganesh A. Robust face Recognition via Sparse Representation ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
［8］ Obozinski G, Taskar B, Jordan M I. Joint Covariate Selection and Joint Subspace Selection for Multiple Classification Problems ［J］. Statistics and Computing, 2010, 20(2): 231-252.
［9］ Lu C Y, Min H, Gui J, et al. Face Recognition via Weighted Sparse Representation ［J］. Journal of Visual Communication and Image Representation, 2013, 24(2): 111-116.
［10］ Kumar A, Chan T S T. Robust Ear Identification Using Sparse Representation of Local Texture Descriptors ［J］. Pattern Recognition, 2013, 46(1): 73-85.
［11］张晓伟, 李明, 左磊. 一种利用稀疏表示的距离扩展目标检测新方法［J］. 西安电子科技大学学报, 2014, 41(3): 24-29.
Zhang Xiaowei, Li Ming, Zuo Lei. New Detection Method for Extended Targets Using Sparse Representation ［J］. Journal of Xidian University, 2014, 41(3): 24-29.
［12］ Zhang S, Yao H, Zhou H, et al. Robust Visual Tracking Based on Online Learning Sparse Representation ［J］. Neurocomputing, 2013, 100: 31-40.
［13］ Zhang S, Yao H, Sun X, et al. Sparse Coding Based Visual Tracking: Review and Experimental Comparison［J］. Pattern Recognition, 2013, 46(7): 1772-1788.
［14］ Zhang H, Nasrabadi N M, Huang T S, et al. Transient Acoustic Signal Classification Using Joint Sparse Representation ［C］//IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2011: 2220-2223.
［15］杨海, 张翔, 梁春燕, 等. 联合因子分析和稀疏表示在稳健性说话入确认中的应用［J］. 声学学报, 2012, 37(5): 548-552.
Yang Hai, Zhang Xiang, Liang Chunyan, et al. Robust Speaker Verification Using Sparse Representation on Joint Factor Analysis ［J］. Acta Acustica, 2012, 37(5): 548-552.
［16］ Tropp J A, Gilbert A C. Signal Recovery from Random Measurements via Orthogonal Matching Pursuit, Information Theory ［J］. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.
［17］刘寅, 吴顺君, 张怀根, 等. 一种快速的基于压缩感知的多普勒高分辨方法［J］. 西安电子科技大学学报, 2011, 38(2): 82-87.
Liu Yin, Wu Shunjun, Zhang Huaigen, et al. Low Complexity Compressed Sensing Based Doppler High Resolution Algorithm ［J］. Journal of Xidian University, 2011, 38(2): 82-87.
［18］ Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition ［J］. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
［19］ Hasan M R, Jamil M, Rahman M G R M S. Speaker Identification Using Mel Frequency Cepstral Coefficients ［C］//3rd International Conference on Electrical ＆ Computer Engineering. Piscataway: IEEE Press, 2004: 565-568.
［20］ Zhang L, Zhou W D, Chang P C, et al. Kernel Sparse Representation-based Classifier ［J］. IEEE Transactions on Signal Processing, 2012, 60(4): 1684-1695.
［21］ Gao S, Tsang I W H, Chia L T. Sparse Representation with Kernels ［J］. IEEE Transactions on Image Processing, 2013, 22(2): 423-434.
［22］ Hsu C W, Chang C C, Lin C J. A Practical Guide to Support Vector Classification［R/OL］. ［2014-02-15］. http://www.csie.ntu.edu.tw／～cjlin／papers／guide／guide.pdf.

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复杂场景下声频传感器网络核稀疏表示车辆识别

Vehicle recognition using acoustic sensor networks in complex scenes via kernel sparse representation

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