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Hough Transform as a Tool for the Classification of Vehicle Speed Changes in On-Road Audio Recordings

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New Frontiers in Mining Complex Patterns (NFMCP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11948))

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Abstract

Spectrogram is a very useful sound representation, showing frequency contents as a function of time. However, the spectrogram data are very complex, as they may contain both lines or curves corresponding to partials (harmonic or not), whose frequency changes in time, as well as noises of various origin. In this paper, we address the extraction of line parameters from spectrograms for audio data, recorded for cars passing by an audio recorder. These lines represent pitched sounds, and the frequency along these lines is usually related to the vehicle speed. Our goal is to detect whether the vehicle is slowing down, speeding, or maintaining approximately constant speed. However, the lines may be broken, they bent when the car is passing the microphone because of the Doppler effect, which is strongest when very close to the microphone, and they are on the noisy background. Our goal was to elaborate a methodology, which extracts a simple representation of parameters of these lines (possibly broken, curvy and in noise), and allows detecting the behavior of drivers when passing the measurements point, e.g. near the radar. Audio data can be very useful here, as they can be recorded at low visibility. The proposed methodology, together with the results for on-road recorded audio data, are presented in this paper. This methodology can be then applied in works on road safety issues.

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Acknowledgments

This work was partially supported by research funds sponsored by the Ministry of Science and Higher Education in Poland.

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Authors and Affiliations

  1. University of Life Sciences in Lublin, Akademicka 13, 20-950, Lublin, Poland

    Elżbieta Kubera & Andrzej Kuranc

  2. Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland

    Alicja Wieczorkowska

Authors
  1. Elżbieta Kubera
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  2. Alicja Wieczorkowska
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  3. Andrzej Kuranc
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Corresponding author

Correspondence to Elżbieta Kubera .

Editor information

Editors and Affiliations

  1. University of Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. University of Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  3. CNR-ICAR, Rende, Italy

    Giuseppe Manco

  4. Federico II University, Naples, Italy

    Elio Masciari

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew Ras

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Kubera, E., Wieczorkowska, A., Kuranc, A. (2020). Hough Transform as a Tool for the Classification of Vehicle Speed Changes in On-Road Audio Recordings. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2019. Lecture Notes in Computer Science(), vol 11948. Springer, Cham. https://doi.org/10.1007/978-3-030-48861-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-48861-1_9

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  • Online ISBN: 978-3-030-48861-1

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