Abstract

This repository is used to transform original files from different trajectory prediction datasets into a uniform format for our trajectory prediction models' training and evaluation. Click the following buttons for more information and details of train/test/validation splits:

🛠️ Codes ⚠️ Dataset & Splits Notes

How To Use

To start training or testing our trajectory prediction models, please click the following button to transform dataset files.

💡 Transform Dataset Files

💡 File Formats & Validate on Your Own Datasets

Supported Models and Datasets

The code for this repository needs to be used along with a specific model's code repository. It currently supports the following trajectory prediction models:

🔗 E-Vertical 🔗 SocialCircle 🔗 SocialCirclePlus

The following datasets are supported to train or test our trajectory prediction models:

  • ETH [1] - UCY [2] Benchmark:
    • 2D Coordinate;
  • Stanford Drone Dataset [3]:
    • 2D Coordinate;
    • 2D Bounding Box;
  • nuScenes [4]:
    • 2D Coordinate;
    • 3D Bounding Box;
    • 3D Bounding Box with Rotation;
  • NBA SportVU [5]:
    • 2D Coordinate;
  • Human3.6M [6,7]:
    • 3D Human Skeleton (17 Points);
  • TBA...
  1. S. Pellegrini, A. Ess, K. Schindler, and L. Van Gool, “You’ll never walk alone: Modeling social behavior for multi-target tracking,” in 2009 IEEE 12th International Conference on Computer Vision. IEEE, 2009, pp. 261–268.
  2. A. Lerner, Y. Chrysanthou, and D. Lischinski, “Crowds by example,” Computer Graphics Forum, vol. 26, no. 3, pp. 655–664, 2007.
  3. A. Robicquet, A. Sadeghian, A. Alahi, and S. Savarese, “Learning social etiquette: Human trajectory understanding in crowded scenes,” in European conference on computer vision. Springer, 2016, pp. 549–565.
  4. A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” arXiv preprint arXiv:1903.11027, 2019.
  5. K. Linou, D. Linou, and M. de Boer, “Nba player movements,” https://github.com/linouk23/NBA-Player-Movements, 2016.
  6. C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu, “Human3.6m: Large scale datasets and predictive methods for 3d humansensing in natural environments,” IEEE transactions on patternanalysis and machine intelligence, vol. 36, no. 7, pp. 1325–1339, 2013.
  7. C. S. Catalin Ionescu, Fuxin Li, “Latent structured models for human pose estimation,” in International Conference on Computer Vision, 2011.
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