Stanford Drone Dataset

Introduction

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.

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.

Publication

  • 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.

Statistics

The dataset consists of eight unique scenes. The number of videos in each scene and the percentage of each agent in each scene is reported below.

Scenes Videos Bicyclist Pedestrian Skateboarder Cart Car Bus
gates 9 51.94 43.36 2.55 0.29 1.08 0.78
little 4 56.04 42.46 0.67 0 0.17 0.67
nexus 12 4.22 64.02 0.60 0.40 29.51 1.25
coupa 4 18.89 80.61 0.17 0.17 0.17 0
bookstore 7 32.89 63.94 1.63 0.34 0.83 0.37
deathCircle 5 56.30 33.13 2.33 3.10 4.71 0.42
quad 4 12.50 87.50 0 0 0 0
hyang 15 27.68 70.01 1.29 0.43 0.50 0.09

License

The datasets provided on this page are published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. If you are interested in commercial usage you can contact us for further options.

Annotation samples

Contact : amirabs at stanford dot edu

Last update : 08/01/2016