Stanford Drone
2D Box Tracking
UAV
|...
许可协议: CC BY-NC-SA 3.0

Overview

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

Please use the following citation when referencing the dataset:

@inproceedings{robicquet2016learning,
  title={Learning social etiquette: Human trajectory understanding in crowded scenes},
  author={Robicquet, Alexandre and Sadeghian, Amir and Alahi, Alexandre and Savarese, Silvio},
  booktitle={European conference on computer vision},
  pages={549--565},
  year={2016},
  organization={Springer}
}

License

CC BY-NC-SA 3.0

数据概要
数据格式
Video,
数据量
--
文件大小
66.13GB
发布方
CVGL(The Computational Vision and Geometry Lab)
The Computational Vision and Geometry Lab (CVGL) at Stanford is directed by Prof. Silvio Savarese. Our research addresses the theoretical foundations and practical applications of computational vision.
数据集反馈
| 219 | 数据量 -- | 大小 66.13GB
Stanford Drone
2D Box Tracking
UAV
许可协议: CC BY-NC-SA 3.0

Overview

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

Please use the following citation when referencing the dataset:

@inproceedings{robicquet2016learning,
  title={Learning social etiquette: Human trajectory understanding in crowded scenes},
  author={Robicquet, Alexandre and Sadeghian, Amir and Alahi, Alexandre and Savarese, Silvio},
  booktitle={European conference on computer vision},
  pages={549--565},
  year={2016},
  organization={Springer}
}

License

CC BY-NC-SA 3.0

数据集反馈
0
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