Initial Evaluation of Vehicle Type Identification using Roadside Stereo Microphones

Billy Dawton; Shigemi Ishida; Yuki Hori; Masato Uchino; Yutaka Arakawa; Shigeaki Tagashira; Akira Fukuda

doi:10.1109/SAS48726.2020.9220076

Initial Evaluation of Vehicle Type Identification using Roadside Stereo Microphones

Billy Dawton, Shigemi Ishida, Yuki Hori, Masato Uchino, Yutaka Arakawa, Shigeaki Tagashira, Akira Fukuda

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

A key feature of Intelligent Transport Systems (ITS) is the ability to detect and identify vehicles. In this paper, we put forward a stereo microphone-based system capable of detecting and identifying the type of individually, sequentially, and simultaneously passing vehicles in multi-lane environments based on their sound. We find that our proposed system shows improved performance over single-microphone systems thanks to its improved sequential and successive vehicle detection performance. Initial evaluation results using sound data collected from roads on a university campus show a classification accuracy of 95.01 %.

Original language	English
Title of host publication	2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728148427
DOIs	https://doi.org/10.1109/SAS48726.2020.9220076
Publication status	Published - Mar 2020
Event	15th IEEE Sensors Applications Symposium, SAS 2020 - Kuala Lumpur, Malaysia Duration: Mar 9 2020 → Mar 11 2020

Publication series

Name	2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings

Conference

Conference	15th IEEE Sensors Applications Symposium, SAS 2020
Country/Territory	Malaysia
City	Kuala Lumpur
Period	3/9/20 → 3/11/20

All Science Journal Classification (ASJC) codes

Computer Science Applications
Computer Vision and Pattern Recognition
Instrumentation

Access to Document

10.1109/SAS48726.2020.9220076

Cite this

Dawton, B., Ishida, S., Hori, Y., Uchino, M., Arakawa, Y., Tagashira, S., & Fukuda, A. (2020). Initial Evaluation of Vehicle Type Identification using Roadside Stereo Microphones. In 2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings [9220076] (2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SAS48726.2020.9220076

Initial Evaluation of Vehicle Type Identification using Roadside Stereo Microphones. / Dawton, Billy; Ishida, Shigemi; Hori, Yuki et al.

2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9220076 (2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Dawton, B, Ishida, S, Hori, Y, Uchino, M, Arakawa, Y, Tagashira, S & Fukuda, A 2020, Initial Evaluation of Vehicle Type Identification using Roadside Stereo Microphones. in 2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings., 9220076, 2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 15th IEEE Sensors Applications Symposium, SAS 2020, Kuala Lumpur, Malaysia, 3/9/20. https://doi.org/10.1109/SAS48726.2020.9220076

Dawton B, Ishida S, Hori Y, Uchino M, Arakawa Y, Tagashira S et al. Initial Evaluation of Vehicle Type Identification using Roadside Stereo Microphones. In 2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9220076. (2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings). doi: 10.1109/SAS48726.2020.9220076

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AB - A key feature of Intelligent Transport Systems (ITS) is the ability to detect and identify vehicles. In this paper, we put forward a stereo microphone-based system capable of detecting and identifying the type of individually, sequentially, and simultaneously passing vehicles in multi-lane environments based on their sound. We find that our proposed system shows improved performance over single-microphone systems thanks to its improved sequential and successive vehicle detection performance. Initial evaluation results using sound data collected from roads on a university campus show a classification accuracy of 95.01 %.

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Initial Evaluation of Vehicle Type Identification using Roadside Stereo Microphones

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