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

Abstract

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

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.

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

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