Acoustic Signal-Based Tunnel Incident Detection System
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (23)
- Abdulhai B, "Enhancing the Universality and Transferability of Freeway Incident Detection Using a Bayesian-Based Neural Network", Trans. Res. Part C, 7(5), pp.261~280 (1999)
- Balke K. N, "An Evaluation of Existing Incident Detection Algorithms", Texas Trans. Ins (1993), The Texas A&M Univ. Sys
- Browne R, "Comparison and Analysis Tool for Automatic Incident Detection", Trans. Res. Rec, (1925) (1925)
- Castro-Neto M. M, "Toward Training-Free Automatic Detection of Freeway Incidents: Simple Algorithm with One Parameter", Trans. Res. Rec, (2278) (2012)
- Clavel C, "Events detection for an audio-based surveillance system", Proc. ICME (2005)
- European Commission, "Safety in Tunnels: Experience Feedback and Regulation Prospection", European Self-Training Workshop (2001)
- Fahrtash M, "Automated Video Incident Detection Systems", Caltrans Div. of Res. and Innov (2012)
- FitzGerald D, "Non-Negative Tensor Factorization for Sound Source Separation", ISSC 2005 (2005)
- Foggia P, "Car crashes detection by audio analysis in crowded roads", Proc. 12th IEEE Int. Con. on Advanced Video and Signal Based Surveillance (2015)
- Gemmeke J, "An Exemplar-Based NMF Approach to Audio Event Detection", Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (2013)
- Ghahramani Z, "An Introduction to Hidden Markov Models and Bayesian Networks", Inter. Jour. of Pattern Recognition and Artificial Intelligence, 15(1), pp.9~41 (2001)
- Hancocks S, "A55 North Wales Tunnels Area Video Automatic Incident Detection", EasyWay, Document number: 5083687, 03, pp.35695, 2011[DOI]
- Harlow C., "Acoustic Accident Detection System", Jour. of Intelli. Trans. Sys, 7(1), pp.43~56 (2002)
- 전광명, "비음수 텐서 분해 및 은닉 마코프 모델을 이용한 다음향 환경에서의 이중 채널 음향 사건 검출", 전자공학회논문지, 54(1), pp.121~128 (2017), 대한전자공학회
- Kim, H, "A Study on the Relationship among Traffic Accidents, Fire Occurrences and Tunnel Characteristics in Local Road Tunnels", Proc. of 2004 Korea Tunnel Eng. Conf (2004)
- 이형석, "영상과 음향 기반의 교차로내 교통사고 검지시스템의 구현", 제어.로봇.시스템학회 논문지, 10(6), pp.501~509 (2004), 제어·로봇·시스템학회
- Neale W, "Evaluation of Discrete Vehicle Accident Sounds for use in Accident Reconstruction", Proc. Mtgs. Acoust, 5(1) (2008)
- Ozbay K, "Incident Management in Intelligent Transportation Systems" (1999), Artech House
- Prevedouros P, "Video Incident Detection Tests in Freeway Tunnels", Trans. Res. Rec, (1959) (1959)
- Rabaoui A, "Using one-class SVMs and wavelets for audio surveillance", IEEE Trans. Inf. Forensics Security, 3(4), pp.763~775 (2008)
- Vacher M, "Sound Detection and Classification for Medical Telesurvey", 2nd Conf. on Bio. Eng (2004)
- Valenzise G, "Scream and Gunshot Detection and Localization for Audio-Surveillance Systems", Proc. of IEEE Conference on Advanced Video and Signal Based Surveillance (2007)
- William B. M, "Traffic Management Center use of Incident Detection Algorithms: Findings of a Nationwide Survey", IEEE Trans. on Intel. Trans. Sys, 8(2), pp.351~358 (2007)