Journal of Advanced Information Technology and Convergence (한국정보기술학회 영문논문지)

Korean Institute of Information Technology (한국정보기술학회)
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Vehicle-Level Traffic Accident Detection on Vehicle-Mounted Camera Based on Cascade Bi-LSTM

  • Son, Hyeon-Cheol (Dept. of Computer Engineering, Kumoh National Institute of Technology) ;
  • Kim, Da-Seul (Dept. of Computer Engineering, Kumoh National Institute of Technology) ;
  • Kim, Sung-Young (Dept. of Computer Engineering, Kumoh National Institute of Technology)
  • Received : 2020.12.14
  • Accepted : 2020.12.30
  • Published : 2020.12.31
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Abstract

In this paper, we propose a traffic accident detection on vehicle-mounted camera. In the proposed method, the minimum bounding box coordinates the central coordinates on the bird's eye view and motion vectors of each vehicle object, and ego-motions of the vehicle equipped with dash-cam are extracted from the dash-cam video. By using extracted 4 kinds features as the input of Bi-LSTM (bidirectional LSTM), the accident probability (score) is predicted. To investigate the effect of each input feature on the probability of an accident, we analyze the performance of the detection the case of using a single feature input and the case of using a combination of features as input, respectively. And in these two cases, different detection models are defined and used. Bi-LSTM is used as a cascade, especially when a combination of the features is used as input. The proposed method shows 76.1% precision and 75.6% recall, which is superior to our previous work.

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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