Journal of Advanced Information Technology and Convergence (한국정보기술학회 영문논문지)

Korean Institute of Information Technology (한국정보기술학회)
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Traffic Accident Detection Based on Ego Motion and Object Tracking

  • Kim, Da-Seul (Dept. of Computer Engineering, Kumoh National Institute of Technology) ;
  • Son, Hyeon-Cheol (Dept. of Computer Engineering, Kumoh National Institute of Technology) ;
  • Si, Jong-Wook (Dept. of Computer Engineering, Kumoh National Institute of Technology) ;
  • Kim, Sung-Young (Dept. of Computer Engineering, Kumoh National Institute of Technology)
  • Received : 2020.06.24
  • Accepted : 2020.07.25
  • Published : 2020.07.31
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Abstract

In this paper, we propose a new method to detect traffic accidents in video from vehicle-mounted cameras (vehicle black box). We use the distance between vehicles to determine whether an accident has occurred. To calculate the position of each vehicle, we use object detection and tracking method. By the way, in a crowded road environment, it is so difficult to decide an accident has occurred because of parked vehicles at the edge of the road. It is not easy to discriminate against accidents from non-accidents because a moving vehicle and a stopped vehicle are mixed on a regular downtown road. In this paper, we try to increase the accuracy of the vehicle accident detection by using not only the motion of the surrounding vehicle but also ego-motion as the input of the Recurrent Neural Network (RNN). We improved the accuracy of accident detection compared to the previous method.

Keywords

Acknowledgement

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Consilience Creative program (IITP-2020-2011-1-00783) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation)

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