Attention R-CNN for Accident Detection

Trung Nghia Le, Shintaro Ono, Akihiro Sugimoto, Hiroshi Kawasaki

Research output: Contribution to conferencePaperpeer-review

20 Citations (Scopus)


This paper addresses accident detection where we not only detect objects with classes, but also recognize their characteristic properties. More specifically, we aim at simultaneously detecting object class bounding boxes on roads and recognizing their status such as safe, dangerous, or crashed. To achieve this goal, we construct a new dataset and propose a baseline method for benchmarking the task of accident detection. We design an accident detection network, called Attention R-CNN, which consists of two streams: one is for object detection with classes and one for characteristic property computation. As an attention mechanism capturing contextual information in the scene, we integrate global contexts exploited from the scene into the stream for object detection. This introduced attention mechanism enables us to recognize object characteristic properties. Extensive experiments on the newly constructed dataset demonstrate the effectiveness of our proposed network. The dataset and source code are publicly available on our project page. 1

Original languageEnglish
Number of pages8
Publication statusPublished - 2020
Event31st IEEE Intelligent Vehicles Symposium, IV 2020 - Virtual, Las Vegas, United States
Duration: Oct 19 2020Nov 13 2020


Conference31st IEEE Intelligent Vehicles Symposium, IV 2020
Country/TerritoryUnited States
CityVirtual, Las Vegas

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Automotive Engineering
  • Modelling and Simulation


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