EPJ Web Conf.
Volume 224, 2019IV International Conference “Modeling of Nonlinear Processes and Systems” (MNPS-2019)
|Number of page(s)||5|
|Section||Machine Learning, Artificial Intelligence and High-Performance Computing|
|Published online||09 December 2019|
Application of the Motion Detector and the Artificial Neural Network to Detect Vehicle Collisions: A Case Study
Murmansk State Technical University, Department of Math, Information Systems and Software Engineering, RU-183010, Murmansk, Russia
2 Murmansk Arctic State University, Near-Earth Environment Computer Modelling Laboratory, RU-183025, Murmansk, Russia
* e-mail: email@example.com
Published online: 9 December 2019
Motor vehicle collisions are a common cause of deaths or/and injuries. The key to lowering the death rate and damages to the health of collision accident victims is a timely arrival of Emergency Services to the accident scene. In the paper, we present and discuss the first results of the design and implementation of the vehicles collision detection system, which is based on a motion detector (MD) and Artificial Neural Network (ANN). To test MD and ANN separately, a small set of video records from traffic cameras that was not a part of a training dataset, were used. We found that while MD demonstrates reasonable performance, Haar Cascades-based pre-trained ANN requires significant improvements. Possible solutions to the aforementioned problem were proposed and discussed.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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