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Publication Detail
CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning
  • Publication Type:
    Journal article
  • Authors:
    Ibrahim MR, Haworth J, Christie N, Cheng T
  • Publisher:
  • Publication date:
  • Journal:
    IET Intelligent Transport Systems
  • Status:
  • Language:
  • Keywords:
    action recognition, computer vision, cycling near misses, deep learning, video streams
  • Notes:
    © 2021 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Cycling is a promising sustainable mode for commuting and leisure in cities. However, the perception of cycling as a risky activity reduces its wide expansion as a commuting mode. A novel method called CyclingNet has been introduced here for detecting cycling near misses from video streams generated by a mounted frontal camera on a bike regardless of the camera position, the conditions of the built environment, the visual conditions and without any restrictions on the riding behaviour. CyclingNet is a deep computer vision model based on a convolutional structure embedded with self-attention bidirectional long-short term memory (LSTM) blocks that aim to understand near misses from both sequential images of scenes and their optical flows. The model is trained on scenes of both safe rides and near misses. After 42 hours of training on a single GPU, the model shows high accuracy on the training, testing and validation sets. The model is intended to be used for generating information that can draw significant conclusions regarding cycling behaviour in cities and elsewhere, which could help planners and policy-makers to better understand the requirement of safety measures when designing infrastructure or drawing policies. As for future work, the model can be pipelined with other state-of-the-art classifiers and object detectors simultaneously to understand the causality of near misses based on factors related to interactions of road users, the built and the natural environments.
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