Abstract
Vehicle recognition and classification have broad applications, ranging from traffic flow management to military target identification. We demonstrate an unsupervised method for automated identification of moving vehicles from roadside audio sensors. Using a short-time Fourier transform to decompose audio signals, we treat the frequency signature in each time window as an individual data point. We then use a spectral embedding for dimensionality reduction. Based on the leading eigenvectors, we relate the performance of an incremental reseeding algorithm to that of spectral clustering. We find that incremental reseeding accurately identifies individual vehicles using their acoustic signatures.
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Acknowledgments
The authors are grateful to Dr. Arjuna Flenner at the US Navy’s Naval Air Systems Command (NAVAIR) for having supplied the vehicle data. We also wish to thank the anonymous reviewers for their comments and suggestions, which helped improve the clarity of the paper.
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Sunu, J., Percus, A.G., Hunter, B. (2018). Unsupervised Vehicle Recognition Using Incremental Reseeding of Acoustic Signatures. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_15
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DOI: https://doi.org/10.1007/978-3-030-01851-1_15
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