Collaborative representation based classification for vehicle recognition in acoustic sensor networks

Abstract

Sparse Representation Classification has led to state-of-the-art results in pattern classification tasks. However, as Sparse Representation Classification has significantly high lower complexity, and vehicle recognition is a typical small-sample-size problem and trained dictionary is under-complete, all these give rise to big representation errors and unstable recognition results. In this paper, we develop a new Collaborative Representation based vehicle recognition framework, using acoustic sensor networks to reduce the time complexity in the training and testing phases, and to improve the classification accuracy in complex scenes. In the recognition, the acoustic signals of vehicles are extracted from the acoustic information to get linearly separable samples by Fast Fourier Transform, and then we encode a testing sample through linear combination of all the training samples with regularized least square and classify the testing sample into the class with the minimum representation error. As demonstrated by experimental results, the proposed method has the following two unique and important characteristics: (1) it achieves a superior performance under the circumstance of complex data sets (2) It also shows highly competitive recognition accuracy while has low computational complexity and memory requirements, compared to k-Nearest Neighbor, Support Vector Machines and Sparse Representation Classification algorithm.