mlsanityData to 7 Oct 2026

Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators

arXiv 2303.15231 ↗First posted 27 Mar 2023cs.CVcited by 4

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Abstract (arXiv)

The application of Computer Vision (CV) techniques massively stimulates microscopic traffic safety analysis from the perspective of traffic conflicts and near misses, which is usually measured using Surrogate Safety Measures (SSM). However, as video processing and traffic safety modeling are two separate research domains and few research have focused on systematically bridging the gap between them, it is necessary to provide transportation researchers and practitioners with corresponding guidance. With this aim in mind, this paper focuses on reviewing the applications of CV techniques in traffic safety modeling using SSM and suggesting the best way forward. The CV algorithm that are used for vehicle detection and tracking from early approaches to the state-of-the-art models are summarized at a high level. Then, the video pre-processing and post-processing techniques for vehicle trajectory extraction are introduced. A detailed review of SSMs for vehicle trajectory data along with their application on traffic safety analysis is presented. Finally, practical issues in traffic video processing and SSM-based safety analysis are discussed, and the available or potential solutions are provided. This review is expected to assist transportation researchers and engineers with the selection of suitable CV techniques for video processing, and the usage of SSMs for various traffic safety research objectives.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 39 of its 145 references that are in the map. First posted on arXiv 27 Mar 2023.

Builds on

39 of 145 references.

References · 39

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  5. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  6. 2020End-to-End Object Detection with Transformerscited by 7,384
  7. 2017Mask R-CNNcited by 7,259
  8. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  9. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  10. 2014Going Deeper with Convolutionscited by 4,231
  11. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  12. 2015Fast R-CNNcited by 2,930
27 more
  1. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  2. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  3. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  4. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  5. 2019Objects as Pointscited by 1,172
  6. 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 908
  7. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 812
  8. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  9. 2019TabNet: Attentive Interpretable Tabular Learningcited by 640
  10. 2018Object Detection with Deep Learning: A Reviewcited by 524
  11. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  12. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
  13. 2019RepPoints: Point Set Representation for Object Detectioncited by 264
  14. 2015BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentationcited by 238
  15. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
  16. 2019Bottom-up Object Detection by Grouping Extreme and Center Pointscited by 156
  17. 2019Feature Selective Anchor-Free Module for Single-Shot Object Detectioncited by 139
  18. 2019The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersectionscited by 137
  19. 2018Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identificationcited by 96
  20. 2022QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Trackingcited by 61
  21. 2022CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twinscited by 43
  22. 2019Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campuscited by 25
  23. 2022Towards Next Generation of Pedestrian and Connected Vehicle In-the-loop Research: A Digital Twin Co-Simulation Frameworkcited by 8
  24. 2018Ground Plane Polling for 6DoF Pose Estimation of Objects on the Roadcited by 6
  25. 2017Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehiclescited by 5
  26. 2022Trajectory Prediction for Vehicle Conflict Identification at Intersections Using Sequence-to-Sequence Recurrent Neural Networkscited by 5
  27. 2020Defining Traffic States using Spatio-temporal Traffic Graphscited by 2

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagerycited by 10
  2. 2024A Unified Probabilistic Approach to Traffic Conflict Detectioncited by 3
  3. 2025UAV-Based Intelligent Traffic Surveillance System: Real-Time Vehicle Detection, Classification, Tracking, and Behavioral Analysiscited by 1
  4. 2025Precision-Aware Video Compression for Reducing Bandwidth Requirements in Video Communication for Vehicle Detection-Based Applicationscited by 0