# CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning

arXiv: https://arxiv.org/abs/2102.00565 · HTML: https://mlsanity.com/paper/2102.00565/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 31 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors](https://mlsanity.com/paper/1505.04868/) (2015)
- [Attentional Pooling for Action Recognition](https://mlsanity.com/paper/1711.01467/) (2017)
- [Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification](https://mlsanity.com/paper/1711.08200/) (2017)
- [Towards Good Practices for Very Deep Two-Stream ConvNets](https://mlsanity.com/paper/1507.02159/) (2015)
- [Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification](https://mlsanity.com/paper/1504.01561/) (2015)
- [Review of Action Recognition and Detection Methods](https://mlsanity.com/paper/1610.06906/) (2016)
- [WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning](https://mlsanity.com/paper/1910.09910/) (2019)
- [URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision](https://mlsanity.com/paper/1809.03609/) (2018)

## Built on by (most cited first, 5 of 5)

- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis](https://mlsanity.com/paper/2407.15199/) (2024)
- [A Benchmark for Cycling Close Pass Detection from Video Streams](https://mlsanity.com/paper/2304.11868/) (2023)
- [FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease](https://mlsanity.com/paper/2308.12840/) (2023)
- [Learning to See Like Humans: Gaze-Aligned Cycling Safety Prediction](https://mlsanity.com/paper/2605.24040/) (2026)

## Abstract (arXiv)

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.