I see you: A Vehicle-Pedestrian Interaction Dataset from Traffic Surveillance Cameras
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Abstract (arXiv)
The development of autonomous vehicles arises new challenges in urban traffic scenarios where vehicle-pedestrian interactions are frequent e.g. vehicle yields to pedestrians, pedestrian slows down due approaching to the vehicle. Over the last years, several datasets have been developed to model these interactions. However, available datasets do not cover near-accident scenarios that our dataset covers. We introduce I see you, a new vehicle-pedestrian interaction dataset that tackles the lack of trajectory data in near-accident scenarios using YOLOv5 and camera calibration methods. I see you consist of 170 near-accident occurrences in seven intersections in Cusco-Peru. This new dataset and pipeline code are available on Github.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 17 Nov 2022.
Builds on
8 of 20 references.
References · 8
- 2019The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersectionscited by 137
- 2019Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campuscited by 25
- 2018An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmarkcited by 16
- 2021Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centerscited by 16
- 2021A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructurecited by 7
- 2020The Adaptability and Challenges of Autonomous Vehicles to Pedestrians in Urban Chinacited by 2
- 2021Turning Traffic Monitoring Cameras into Intelligent Sensors for Traffic Density Estimationcited by 2
- 2017Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibrationcited by 1
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Citing papers per year: 2024: 2.
2 papers.