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A Benchmark for Cycling Close Pass Detection from Video Streams

https://doi.org/10.2139/ssrn.4773028
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Two-Frame Motion Estimation Based on Polynomial Expansion
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Cyclists and drivers in road interactions: A comparison of perceived crash risk
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Predictors of the frequency and subjective experience of cycling near misses: Findings from the first two years of the UK Near Miss Project
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How much space do drivers provide when passing cyclists? Understanding the impact of motor vehicle and infrastructure characteristics on passing distance
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A real-time video surveillance system for traffic pre-events detection
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Subjective experiences of bicyclists being passed by motor vehicles: The relationship to motor vehicle passing distance
inferredcites →https://doi.org/10.1016/j.jsr.2021.09.005openalex-extracted
Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle–bicycle crashes
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Investigating the rates and impacts of near misses and related incidents among UK cyclists
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Implementing cycling infrastructure in a politicized space: Lessons from Toronto, Canada
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Knowledge driven temporal activity localization
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V-LPDR: Towards a unified framework for license plate detection, tracking, and recognition in real-world traffic videos
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Edge computing enabled video segmentation for real-time traffic monitoring in internet of vehicles
inferredcites →https://doi.org/10.1016/j.tra.2016.04.016openalex-extracted
Cycling near misses: Their frequency, impact, and prevention
inferredcites →https://doi.org/10.1016/j.trc.2021.103191openalex-extracted
Markov-game modeling of cyclist-pedestrian interactions in shared spaces: A multi-agent adversarial inverse reinforcement learning approach
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Improving short-term bike sharing demand forecast through an irregular convolutional neural network
inferredcites →https://doi.org/10.1016/j.trc.2022.103990openalex-extracted
Bicycle sharing station planning: From free-floating to geo-fencing
inferredcites →https://doi.org/10.1016/j.trc.2023.104120openalex-extracted
An automated driving systems data acquisition and analytics platform
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A two-layer integrated model for cyclist trajectory prediction considering multiple interactions with the environment
inferredcites →https://doi.org/10.1016/j.trc.2023.104451openalex-extracted
Analyzing scenario criticality and rider’s intervention behavior during high-level autonomous driving: A VR-enabled approach and empirical insights
inferredcites →https://doi.org/10.1016/j.trf.2015.04.006openalex-extracted
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-extracted
A hazard-perception test for cycling children: An exploratory study
inferredcites →https://doi.org/10.1016/j.ypmed.2013.05.028openalex-extracted
The impact of implementing a public bicycle share program on the likelihood of collisions and near misses in Montreal, Canada
inferredcites →https://doi.org/10.1049/itr2.12101openalex-extracted
CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning
inferredcites →https://doi.org/10.1080/01441647.2020.1840456openalex-extracted
Cycling near misses: a review of the current methods, challenges and the potential of an AI-embedded system
inferredcites →https://doi.org/10.1080/01441647.2021.1915898openalex-extracted
Cycling behaviour in 17 countries across 6 continents: levels of cycling, who cycles, for what purpose, and how far?
inferredcites →https://doi.org/10.1080/01441647.2022.2113570openalex-extracted
Adults’ self-reported barriers and enablers to riding a bike for transport: a systematic review
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-extracted
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2015.7299059openalex-extracted
Action recognition with trajectory-pooled deep-convolutional descriptors
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-extracted
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.106openalex-extracted
Feature Pyramid Networks for Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-extracted
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.502openalex-extracted
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
inferredcites →https://doi.org/10.1109/cvpr.2017.597openalex-extracted
3D Bounding Box Estimation Using Deep Learning and Geometry
inferredcites →https://doi.org/10.1109/cvpr.2018.00249openalex-extracted
Multi-level Fusion Based 3D Object Detection from Monocular Images
inferredcites →https://doi.org/10.1109/cvpr.2019.00111openalex-extracted
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
inferredcites →https://doi.org/10.1109/cvpr.2019.00217openalex-extracted
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape
inferredcites →https://doi.org/10.1109/cvpr.2019.00469openalex-extracted
PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01164openalex-extracted
nuScenes: A Multimodal Dataset for Autonomous Driving
inferredcites →https://doi.org/10.1109/cvpr52688.2022.02000openalex-extracted
Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation
inferredcites →https://doi.org/10.1109/iccv.2015.510openalex-extracted
Learning Spatiotemporal Features with 3D Convolutional Networks
inferredcites →https://doi.org/10.1109/iccv.2017.324openalex-extracted
Focal Loss for Dense Object Detection
inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-extracted
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1109/iccv.2017.89openalex-extracted
Deformable Convolutional Networks
inferredcites →https://doi.org/10.1109/iccvw54120.2021.00107openalex-extracted
FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection
inferredcites →https://doi.org/10.1109/lgrs.2019.2963065openalex-extracted
GIS-Supervised Building Extraction With Label Noise-Adaptive Fully Convolutional Neural Network
inferredcites →https://doi.org/10.1109/tits.2020.2997352openalex-extracted
A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-extracted
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1109/tpami.2022.3181116openalex-extracted
Video Pivoting Unsupervised Multi-Modal Machine Translation
inferredcites →https://doi.org/10.1136/bmj.j1456openalex-extracted
Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-extracted
Long Short-Term Memory
inferredcites →https://doi.org/10.1177/0278364913491297openalex-extracted
Vision meets robotics: The KITTI dataset
inferredcites →https://doi.org/10.2139/ssrn.4032616openalex-extracted
Improving Short-Term Bike Sharing Demand Forecast Through an Irregular Convolutional Neural Network
inferredcites →https://doi.org/10.25368/2022.502openalex-extracted
Too close? lnvestigating the distance between cars and bikes when overtaking with regards to the infrastructure using the OpenBikeSensor and information from OpenStreetMap
inferredcites →https://doi.org/10.31235/osf.io/78qgfopenalex-extracted
Spatial variation in bicycling: a retrospective review of travel survey data from Greater Melbourne, Australia
inferredcites →https://doi.org/10.3390/electronics10040517openalex-extracted
A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-extracted
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.48550/arxiv.1212.0402openalex-extracted
UCF-101: A dataset of 101 human actions classes from videos in the wild
inferredcites →https://doi.org/10.48550/arxiv.1406.2199openalex-extracted
Two-Stream Convolutional Networks for Action Recognition in Videos
inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-extracted
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
inferredcites →https://doi.org/10.48550/arxiv.1706.03762openalex-extracted
Attention Is All You Need
inferredcites →https://doi.org/10.48550/arxiv.1711.03938openalex-extracted
CARLA: An Open Urban Driving Simulator
inferredcites →https://doi.org/10.48550/arxiv.1912.01703openalex-extracted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
inferredcites →https://doi.org/10.48550/arxiv.2102.05095openalex-extracted
Is Space-Time Attention All You Need for Video Understanding?
inferredcites →https://doi.org/10.48550/arxiv.2105.03827openalex-extracted
Good Practices and A Strong Baseline for Traffic Anomaly Detection
inferredcites →https://doi.org/10.5694/mja17.00015openalex-extracted
Road safety: serious injuries remain a major unsolved problem
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