The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

Acoustic Signal-Based Tunnel Incident Detection System

음향신호 기반 터널 돌발상황 검지시스템

  • Jang, Jinhwan (Dept. of Highway Res., Korea Inst. of Civil Eng. and Building Tech.)
  • 장진환 (한국건설기술연구원 도로연구소)
  • Received : 2019.06.25
  • Accepted : 2019.09.27
  • Published : 2019.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

An acoustic signal-based, tunnel-incident detection system was developed and evaluated. The system was comprised of three components: algorithm, acoustic signal collector, and server system. The algorithm, which was based on nonnegative tensor factorization and a hidden Markov model, processes the acoustic signals to attenuate noise and detect incident-related signals. The acoustic signal collector gathers the tunnel sounds, digitalizes them, and transmits the digitalized acoustic signals to the center server. The server system issues an alert once the algorithm identifies an incident. The performance of the system was evaluated thoroughly in two steps: first, in a controlled tunnel environment using the recorded incident sounds, and second, in an uncontrolled tunnel environment using real-world incident sounds. As a result, the detection rates ranged from 80 to 95% at distances from 50 to 10 m in the controlled environment, and 94 % in the uncontrolled environment. The superiority of the developed system to the existing video image and loop detector-based systems lies in its instantaneous detection capability with less than 2 s.

본 연구에서는 음향신호 처리기반 터널 돌발상황 탐지시스템을 개발하고 평가하였다. 개발 시스템은 알고리즘, 음향신호 수집기, 서버시스템 세 가지 구성 요소로 구성된다. 비음수 텐서 분해와 은닉 마코프 모델을 이용하여 돌발상황음(충돌, 스키드)을 검출한다. 개발시스템 성능은 제한된 환경과 실제 운영환경에서 평가되었다. 그 결과, 제한된 환경 평가에서 거리별로 80~95%의 검지성능을 보였고, 실제 운영환경에서는 94% 검지성능을 보였다. 기존의 터널 돌발상황 검지기술인 영상 및 루프검지기 기반 시스템 성능과 비교한 결과, 본 개발 기술의 장점은 신속한 검지시간(2초 이내)인 것으로 나타났다.

Keywords

References

  1. Abdulhai B. and Ritchie S.(1999), "Enhancing the Universality and Transferability of Freeway Incident Detection Using a Bayesian-Based Neural Network," Trans. Res. Part C, vol. 7, no. 5, pp.261-280. https://doi.org/10.1016/S0968-090X(99)00022-4
  2. Balke K. N.(1993), An Evaluation of Existing Incident Detection Algorithms, Texas Trans. Ins., The Texas A&M Univ. Sys.
  3. Browne R., Foo S., Huynh S., Abdulhai B. and Hall F.(2005), "Comparison and Analysis Tool for Automatic Incident Detection," Trans. Res. Rec., No. 1925.
  4. Castro-Neto M. M., Han L. D., Jeong Y. S. and Jeong M. K.(2012), "Toward Training-Free Automatic Detection of Freeway Incidents: Simple Algorithm with One Parameter," Trans. Res. Rec., No. 2278.
  5. Clavel C., Ehrette T. and Richard, G.(2005), "Events detection for an audio-based surveillance system," Proc. ICME.
  6. European Commission(2001), European Self-Training Workshop, Safety in Tunnels: Experience Feedback and Regulation Prospection.
  7. Fahrtash M.(2012), "Automated Video Incident Detection Systems," Caltrans Div. of Res. and Innov.
  8. FitzGerald D., Cranitch M. and Coyle E.(2005), "Non-Negative Tensor Factorization for Sound Source Separation," ISSC 2005, Dublin.
  9. Foggia P., Saggese A., Strissciuglio N. and Vento M.(2015), "Car crashes detection by audio analysis in crowded roads," Proc. 12th IEEE Int. Con. on Advanced Video and Signal Based Surveillance.
  10. Gemmeke J., Vuegen P. and Vanrumste B.(2013), "An Exemplar-Based NMF Approach to Audio Event Detection," Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New York, USA.
  11. Ghahramani Z.(2001), "An Introduction to Hidden Markov Models and Bayesian Networks," Inter. Jour. of Pattern Recognition and Artificial Intelligence, vol. 15, no. 1, pp.9-41. https://doi.org/10.1142/S0218001401000836
  12. Hancocks S. and Evans S.(2011), A55 North Wales Tunnels Area Video Automatic Incident Detection, EasyWay, Document number: 5083687/493/03/35695.
  13. Harlow C. and Wang, Y.(2002), "Acoustic Accident Detection System," Jour. of Intelli. Trans. Sys. vol. 7, no. 1, pp.43-56. https://doi.org/10.1080/713930746
  14. Jeon K. and Kim H.(2017), "Dual-Channel Acoustic Event Detection in Multisource Environments Using Nonnegative Tensor Factorization and Hidden Markov Model," Jour. of Inst. of Electro. and Infor. Eng., vol. 54, no. 1, pp.121-128. https://doi.org/10.5573/IEIE.2017.54.1.121
  15. Kim, H. and Lee, C.(2004), "A Study on the Relationship among Traffic Accidents, Fire Occurrences and Tunnel Characteristics in Local Road Tunnels," Proc. of 2004 Korea Tunnel Eng. Conf., South Korea (in Korean).
  16. Lee H., Kim Y., Kwon T., Park K., Bok K. and Han M.(2004), "An Implementation of Traffic Accident Detection System at Intersection Based on Image and Sound," Jour. of Control, Auto. and Sys. Eng., vol. 10, no. 6, pp.501-509.
  17. Neale W., Terpstra T. and Bortles W.(2008), "Evaluation of Discrete Vehicle Accident Sounds for use in Accident Reconstruction," Proc. Mtgs. Acoust., vol. 5, no. 1.
  18. Ozbay K. and Kachroo P.(1999), Incident Management in Intelligent Transportation Systems, Artech House, ISBN 0-89006-774-0, Boston, London.
  19. Prevedouros P., Ji X., Papandreou K., Kopelias P. and Vegiri, V.(2006), "Video Incident Detection Tests in Freeway Tunnels," Trans. Res. Rec., No. 1959.
  20. Rabaoui A., Davy M., Rossignol S. and Ellouze, N.(2008), "Using one-class SVMs and wavelets for audio surveillance," IEEE Trans. Inf. Forensics Security, vol. 3, no. 4, pp.763-775. https://doi.org/10.1109/TIFS.2008.2008216
  21. Vacher M., Istrate D., Besacier L., Serignat J. F. and Castelli, E.(2004), "Sound Detection and Classification for Medical Telesurvey," 2nd Conf. on Bio. Eng., Austria.
  22. Valenzise G., Gerosa L., Tagliasacchi M., Antonacci F. and Sarti, A.(2007), "Scream and Gunshot Detection and Localization for Audio-Surveillance Systems," Proc. of IEEE Conference on Advanced Video and Signal Based Surveillance, London, UK.
  23. William B. M. and Guin A.(2007), "Traffic Management Center use of Incident Detection Algorithms: Findings of a Nationwide Survey," IEEE Trans. on Intel. Trans. Sys., vol. 8, no. 2, pp.351-358. https://doi.org/10.1109/TITS.2007.894193