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

https://doi.org/10.1049/itr2.12101
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/archopht.1952.01700030123016openalex-snapshot
Perception of the Visual World
inferredcites →https://doi.org/10.1007/3-540-45103-x_50openalex-snapshot
Two-Frame Motion Estimation Based on Polynomial Expansion
inferredcites →https://doi.org/10.1016/j.aap.2012.09.005openalex-snapshot
Cyclists and drivers in road interactions: A comparison of perceived crash risk
inferredcites →https://doi.org/10.1016/j.aap.2013.04.018openalex-snapshot
Injury incidence rates of cyclists compared to pedestrians, car occupants and powered two-wheeler riders, using a medical registry and mobility data, Rhône County, France
inferredcites →https://doi.org/10.1016/j.aap.2013.04.037openalex-snapshot
Cyclist activity and injury risk analysis at signalized intersections: A Bayesian modelling approach
inferredcites →https://doi.org/10.1016/j.aap.2014.11.004openalex-snapshot
Perceived traffic risk for cyclists: The impact of near miss and collision experiences
inferredcites →https://doi.org/10.1016/j.aap.2017.09.015openalex-snapshot
Predictors of the frequency and subjective experience of cycling near misses: Findings from the first two years of the UK Near Miss Project
inferredcites →https://doi.org/10.1016/j.cities.2019.102481openalex-snapshot
Understanding cities with machine eyes: A review of deep computer vision in urban analytics
inferredcites →https://doi.org/10.1016/j.injury.2011.10.016openalex-snapshot
Bicycle accidents – Do we only see the tip of the iceberg?
inferredcites →https://doi.org/10.1016/j.jth.2017.02.010openalex-snapshot
Cycling safety: Quantifying the under reporting of cycling incidents in Vancouver, British Columbia
inferredcites →https://doi.org/10.1016/j.socscimed.2011.01.033openalex-snapshot
Cycling and the city: A case study of how gendered, ethnic and class identities can shape healthy transport choices
inferredcites →https://doi.org/10.1016/j.ssci.2013.04.010openalex-snapshot
Cyclists and open vehicle doors: Crash characteristics and risk factors
inferredcites →https://doi.org/10.1016/j.ssci.2015.07.027openalex-snapshot
The German Naturalistic Cycling Study – Comparing cycling speed of riders of different e-bikes and conventional bicycles
inferredcites →https://doi.org/10.1016/j.tra.2016.04.016openalex-snapshot
Cycling near misses: Their frequency, impact, and prevention
inferredcites →https://doi.org/10.1016/j.trf.2014.04.001openalex-snapshot
Introducing naturalistic cycling data: What factors influence bicyclists’ safety in the real world?
inferredcites →https://doi.org/10.1016/j.trf.2015.04.003openalex-snapshot
Using naturalistic data to assess e-cyclist behavior
inferredcites →https://doi.org/10.1016/j.trf.2015.04.006openalex-snapshot
Evaluating bicyclists’ risk perception using video clips: Comparison of frequent and infrequent city cyclists
inferredcites →https://doi.org/10.1016/j.trf.2016.05.001openalex-snapshot
A hazard-perception test for cycling children: An exploratory study
inferredcites →https://doi.org/10.1016/j.ypmed.2009.07.028openalex-snapshot
Infrastructure, programs, and policies to increase bicycling: An international review
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1049/iet-cvi.2018.5830openalex-snapshot
Attention‐based spatial–temporal hierarchical ConvLSTM network for action recognition in videos
inferredcites →https://doi.org/10.1049/iet-its.2018.0064openalex-snapshot
Deep learning methods in transportation domain: a review
inferredcites →https://doi.org/10.1080/01441647.2020.1840456openalex-snapshot
Cycling near misses: a review of the current methods, challenges and the potential of an AI-embedded system
inferredcites →https://doi.org/10.1080/15389588.2013.781591openalex-snapshot
Bicycle Crashes in Different Riding Environments in the Australian Capital Territory
inferredcites →https://doi.org/10.1109/cvpr.2014.471openalex-snapshot
2D Human Pose Estimation: New Benchmark and State of the Art Analysis
inferredcites →https://doi.org/10.1109/cvpr.2015.7299059openalex-snapshot
Action recognition with trajectory-pooled deep-convolutional descriptors
inferredcites →https://doi.org/10.1109/cvpr.2015.7299101openalex-snapshot
Beyond short snippets: Deep networks for video classification
inferredcites →https://doi.org/10.1109/cvpr.2016.119openalex-snapshot
Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs
inferredcites →https://doi.org/10.1109/cvpr.2017.675openalex-snapshot
SST: Single-Stream Temporal Action Proposals
inferredcites →https://doi.org/10.1109/iccv.2011.6126543openalex-snapshot
HMDB: A large video database for human motion recognition
inferredcites →https://doi.org/10.1109/iccv.2015.123openalex-snapshot
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
inferredcites →https://doi.org/10.1109/iiphdw.2018.8388338openalex-snapshot
Data augmentation for improving deep learning in image classification problem
inferredcites →https://doi.org/10.1136/injuryprev-2011-040160openalex-snapshot
Exposure-based cycling crash, near miss and injury rates: The Safer Cycling Prospective Cohort Study protocol: Figure 1
inferredcites →https://doi.org/10.1145/2733373.2806222openalex-snapshot
Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification
inferredcites →https://doi.org/10.1177/2399808319846517openalex-snapshot
URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision
inferredcites →https://doi.org/10.1289/ehp.0901747openalex-snapshot
Do the Health Benefits of Cycling Outweigh the Risks?
inferredcites →https://doi.org/10.4000/books.pur.52937openalex-snapshot
Put the blame on…?
inferredcites →https://doi.org/10.48550/arxiv.1406.2199openalex-snapshot
Two-Stream Convolutional Networks for Action Recognition in Videos
inferredcites →https://doi.org/10.48550/arxiv.1502.03044openalex-snapshot
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
inferredcites →https://doi.org/10.48550/arxiv.1507.02159openalex-snapshot
Towards Good Practices for Very Deep Two-Stream ConvNets
inferredcites →https://doi.org/10.48550/arxiv.1610.06906openalex-snapshot
Review of Action Recognition and Detection Methods
inferredcites →https://doi.org/10.48550/arxiv.1706.03762openalex-snapshot
Attention Is All You Need
inferredcites →https://doi.org/10.48550/arxiv.1711.08200openalex-snapshot
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
inferredcites →https://doi.org/10.7551/mitpress/8291.003.0026openalex-snapshot
Adaptive Computation and Machine Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4773028openalex-extracted
A Benchmark for Cycling Close Pass Detection from Video Streams
inferred← citeshttps://doi.org/10.1371/journal.pone.0288670openalex-snapshot
FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases
inferred← citeshttps://doi.org/10.23919/jcn.2022.000007openalex-snapshot
Development of a cycling safety services system and its deep learning bicycle crash model
inferred← citeshttps://doi.org/10.48550/arxiv.2008.00549openalex-snapshot
Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications
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