# I see you: A Vehicle-Pedestrian Interaction Dataset from Traffic Surveillance Cameras

arXiv: https://arxiv.org/abs/2211.09342 · HTML: https://mlsanity.com/paper/2211.09342/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus](https://mlsanity.com/paper/1902.00487/) (2019)
- [An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark](https://mlsanity.com/paper/1805.07663/) (2018)
- [Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers](https://mlsanity.com/paper/2106.12442/) (2021)
- [A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure](https://mlsanity.com/paper/2105.02572/) (2021)
- [The Adaptability and Challenges of Autonomous Vehicles to Pedestrians in Urban China](https://mlsanity.com/paper/2007.13281/) (2020)
- [Turning Traffic Monitoring Cameras into Intelligent Sensors for Traffic Density Estimation](https://mlsanity.com/paper/2111.00941/) (2021)
- [Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration](https://mlsanity.com/paper/1708.06831/) (2017)

## Built on by (most cited first, 2 of 2)

- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [A Universal Protocol to Benchmark Camera Calibration for Sports](https://mlsanity.com/paper/2404.09807/) (2024)

## 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.