• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.805-808
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• 2008

In this paper, it was performed the analysis on various intersection acoustic patterns for detection rate improvement of accident sound detection system : an acoustic pattern analysis on general traffic noise, an acoustic pattern analysis on engine noise, an acoustic pattern analysis on obstruct factors for accident sound detection system. There are remarkable differences between the acoustic patterns of traffic noise and accident sound, and we most consider the acoustic patterns when we compose the accident traffic detection system by acoustic because there is error range of 20[dB] according to the volume of traffic in intersection.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.9
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• pp.265-273
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• 2018

In this paper, we propose an acoustic event detection method in tunnels using non-negative tensor factorization (NTF) and hidden Markov model (HMM) applied to multi-channel audio signals. Incidents in tunnel are inherent to the system and occur unavoidably with known probability. Incidents can easily happen minor accidents and extend right through to major disaster. Most incident detection systems deploy visual incident detection (VID) systems that often cause false alarms due to various constraints such as night obstacles and a limit of viewing angle. To this end, the proposed method first tries to separate and detect every acoustic event, which is assumed to be an in-tunnel incident, from noisy acoustic signals by using an NTF technique. Then, maximum likelihood estimation using Gaussian mixture model (GMM)-HMMs is carried out to verify whether or not each detected event is an actual incident. Performance evaluation shows that the proposed method operates in real time and achieves high detection accuracy under simulated tunnel conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1142-1148
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• 2006

Actually, a present traffic accident detection system is subsisting limitation of accurate distinction under the crowded condition at intersection because the system depend upon mainly the image information at intersection and digital image processing techniques nearly all. To complement this insufficiency, this article aims to estimate the level of present technology and a realistic possibility by analyzing the acoustic characteristic of crash sound that we have to investigate fur improvement of traffic accident detection rate at intersection. The skid sound of traffic accident was showed the special pattern at 1[KHz])$\sim$3[KHz] bandwidth when vehicles are almost never operated in and around intersection. Also, the frequency bandwidth of vehicle crash sound was showed sound pressure difference over 30[dB] higher than when there is no occurrence of traffic accident below 500[Hz].

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.789-792
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• 2008

Actually, The present traffic accident detection system is subsisting limitation of accurate distinction under the crowded condition at intersection because the system depend upon mainly the image information at intersection and digital image processing techniques nearly all. To complement this insufficiency, this article aims to estimate the level of present technology and a realistic possibility by analyzing the acoustic characteristic of crash sound that we have to investigate for improvement of traffic accident detection rate at intersection. The skid sound of traffic accident is showed the special pattern at $1[kHz]{\sim}3[kHz}$ bandwidth when vehicles are almost never operated in and around intersection. Also, the frequency bandwidth of vehicle crash sound is showed sound pressure difference over 30[dB] higher than when there is no occurrence of traffic accident below 500[Hz].

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.501-509
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• 2004

The frequency of car accidents is very high at the intersection. Because of the state of a traffic signal, quarrels happen after accidents. At night many cars run away after causing an accident. In this case, accident analyses have been conducted by investigating evidences such as eyewitness accounts, tire tracks, fragments of the car or collision traces of the car. But these evidences that don't have enough objectivity cause an error in judgment. In the paper, when traffic accidents happen, the traffic accident detection system that stands on the basis of images and sounds detects traffic accidents to acquire abundant evidences. And, this system transmits 10 seconds images to the traffic center through the wired net and stores images to the Smart Media Card. This can be applied to various ways such as accident management, accident DB construction, urgent rescue after awaring the accident, accident detection in tunnel and in inclement weather.