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http://dx.doi.org/10.12815/kits.2019.18.4.58

A Study on Deep Learning-based Pedestrian Detection and Alarm System  

Kim, Jeong-Hwan (Dept. of Computer Science & Eng., Seoul National Univ. of Science & Technology)
Shin, Yong-Hyeon (Dept. of Computer Science & Eng., Seoul National Univ. of Science & Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.18, no.4, 2019 , pp. 58-70 More about this Journal
Abstract
In the case of a pedestrian traffic accident, it has a large-scale danger directly connected by a fatal accident at the time of the accident. The domestic ITS is not used for intelligent risk classification because it is used only for collecting traffic information despite of the construction of good quality traffic infrastructure. The CNN based pedestrian detection classification model, which is a major component of the proposed system, is implemented on an embedded system assuming that it is installed and operated in a restricted environment. A new model was created by improving YOLO's artificial neural network, and the real-time detection speed result of average accuracy 86.29% and 21.1 fps was shown with 20,000 iterative learning. And we constructed a protocol interworking scenario and implementation of a system that can connect with the ITS. If a pedestrian accident prevention system connected with ITS will be implemented through this study, it will help to reduce the cost of constructing a new infrastructure and reduce the incidence of traffic accidents for pedestrians, and we can also reduce the cost for system monitoring.
Keywords
Pedestrian traffic accident prevention; CNN; YOLO; ITS; UTIS;
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Times Cited By KSCI : 2  (Citation Analysis)
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