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Lim, Sunghoon
Unstructured Data Mining and Machine Learning Lab
Research Interests
  • Unstructured Data Mining, Machine Learning, Industrial Artificial Intelligence (AI+X)

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Car crash detection using ensemble deep learning and multimodal data from dashboard cameras

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dc.contributor.author Choi, Jae Gyeong ko
dc.contributor.author Kong, Chan Woo ko
dc.contributor.author Kim, Gyeongho ko
dc.contributor.author Lim, Sunghoon ko
dc.date.available 2021-06-24T08:16:06Z -
dc.date.created 2021-06-19 ko
dc.date.issued 2021-11 ko
dc.identifier.citation EXPERT SYSTEMS WITH APPLICATIONS, v.183, pp.115400 ko
dc.identifier.issn 0957-4174 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/53109 -
dc.description.abstract Due to the increase in motor vehicle accidents, there is a growing need for high-performance car crash detection systems. The authors of this research propose a car crash detection system that uses both video data and audio data from dashboard cameras in order to improve car crash detection performance. While most existing car crash detection systems depend on single modal data (i.e., video data or audio data only), the proposed car crash detection system uses an ensemble deep learning model based on multimodal data (i.e., both video and audio data), because different types of data extracted from one information source (e.g., dashboard cameras) can be regarded as different views of the same source. These different views complement one another and improve detection performance, because one view may have information that the other view does not contain. In this research, deep learning techniques, gated recurrent unit (GRU) and convolutional neural network (CNN), are used to develop a car crash detection system. A weighted average ensemble is used as an ensemble technique. The proposed car crash detection system, which is based on multiple classifiers that use both video and audio data from dashboard cameras, is validated using a comparison with single classifiers that use video data or audio data only. Car accident YouTube clips are used to validate this research. The experimental results indicate that the proposed car crash detection system performs significantly better than single classifiers. It is expected that the proposed car crash detection system can be used as part of an emergency road call service that recognizes traffic accidents automatically and allows immediate rescue after transmission to emergency recovery agencies. ko
dc.language 영어 ko
dc.publisher Pergamon Press Ltd. ko
dc.title Car crash detection using ensemble deep learning and multimodal data from dashboard cameras ko
dc.type ARTICLE ko
dc.type.rims ART ko
dc.identifier.doi 10.1016/j.eswa.2021.115400 ko
dc.identifier.url https://www.sciencedirect.com/science/article/pii/S095741742100823X?via%3Dihub ko
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