  Cycling is a promising sustainable mode for commuting and leisure in cities,
however, the fear of getting hit or fall reduces its wide expansion as a
commuting mode. In this paper, we introduce a novel method called CyclingNet
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, the visual conditions and without any restrictions on the riding
behaviour. CyclingNet is a deep computer vision model based on 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.
