TRAF: The Dense and Heterogeneous Traffic Dataset.


Description

We provide a dataset of dense and heterogeneous traffic videos. The dataset consists of the following road-agent categories – car, bus, truck, rickshaw, pedestrian, scooter, motorcycle, and other roadagents such as carts and animals. Overall, the dataset contains approximately 13 motorized vehicles, 5 pedestrians and 2 bicycles per frame, respectively. Annotations were performed following a strict protocol and each annotated video file consists of spatial coordinates in pixels, an agent ID, and an agent type. The dataset is categorized according to camera viewpoint (front-facing/top-view), motion (moving/static), time of day (day/evening/night), and difficulty level. The dataset consists of RGB videos with 720p resolution.

Dataset

The dataset can be found here. Please cite this paper if you found the dataset useful:

@InProceedings{Chandra_2019_CVPR,
author = {Chandra, Rohan and Bhattacharya, Uttaran and Bera, Aniket and Manocha, Dinesh},
title = {TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

We show example videos. Can you spot the cows !?:

Paper

Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, and Dinesh Manocha, TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions, CVPR 2019.

Acknowledgements

We thank the following people for contributing to this dataset:

  • Dr. Rahul Kala (IIIT Allahabad)
  • Abhinav Malviya (IIIT Allahabad)
  • Dr. Saket Anand (IIIT Delhi)

Special thanks to Tianrui Guan, Christopher Yue, Vishal Hundal, Christian Roncal, and Xiaoyu Li, for helping with the painstaking task of annotating the dataset !

License

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.