@article{,
title= {Stanford Drone Dataset},
keywords= {},
author= {A. Robicquet and A. Sadeghian and A. Alahi and S. Savarese},
abstract= {When humans navigate a crowed space such as a university campus or the sidewalks of a busy street, they follow common sense rules based on social etiquette. In order to enable the design of new algorithms that can fully take advantage of these rules to better solve tasks such as target tracking or trajectory forecasting, we need to have access to better data. To that end, we contribute the very first large scale dataset (to the best of our knowledge) that collects images and videos of various types of agents (not just pedestrians, but also bicyclists, skateboarders, cars, buses, and golf carts) that navigate in a real world outdoor environment such as a university campus. In the above images, pedestrians are labeled in pink, bicyclists in red, skateboarders in orange, and cars in green.

https://i.imgur.com/iJl5sUN.png

https://i.imgur.com/XOBHAoE.png

https://i.imgur.com/MDruCEV.png

https://i.imgur.com/cYpHgG5.png


### CITATION

If you find this dataset useful, please cite this paper (and refer the data as Stanford Drone Dataset or SDD):
A. Robicquet, A. Sadeghian, A. Alahi, S. Savarese, Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes in European Conference on Computer Vision (ECCV), 2016.},
terms= {},
license= {Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License},
superseded= {},
url= {http://cvgl.stanford.edu/projects/uav_data/}
}