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BIWI RGBD-ID dataset download:

  • the dataset as compressed rar files: training set (50 people), testing set (28 people)
  • further information on the dataset and some Matlab files for reading and visualizing the dataset files are present in this zip folder. 

If you use the BIWI RGBD-ID dataset, please cite the following works:

M. Munaro, A. Fossati, A. Basso, E. Menegatti and L. Van Gool.
"One-Shot Person Re-Identification with a Consumer Depth Camera".
Book Chapter in "Person Re-Identification", pp 161-181, ISBN: 978-1-4471-6295-7, ISSN: 2191-6586, Springer, 2014.

M. Munaro, A. Basso, A. Fossati, L. Van Gool and E. Menegatti.
"3D Reconstruction of freely moving persons for re-identification with a depth sensor".
In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Hong Kong (China), pp. 4512-4519, 2014.

 

IAS-Lab RGBD-ID dataset download:

  • the dataset as compressed rar files: Training, TestingA and TestingB.
  • further information on the dataset and some Matlab files for reading and visualizing the dataset files are present in this zip folder. 

If you use the IAS-Lab RGBD-ID dataset, please cite the following works:

M. Munaro, S. Ghidoni, D. Tartaro Dizmen and E. Menegatti.
"A feature-based approach to people re-identification using skeleton keypoints".
In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Hong Kong (China), pp. 5644-5651, 2014.

M. Munaro, A. Basso, A. Fossati, L. Van Gool and E. Menegatti.
"3D Reconstruction of freely moving persons for re-identification with a depth sensor".
In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Hong Kong (China), pp. 4512-4519, 2014.

 

CAVIAR4REID skeleton annotations:

  • the skeleton annotations as compressed rar files: annotations.

If you use our CAVIAR4REID skeleton annotations, please cite the following work:

M. Munaro, S. Ghidoni, D. Tartaro Dizmen and E. Menegatti.
"A feature-based approach to people re-identification using skeleton keypoints".
In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Hong Kong (China), pp. 5644-5651, 2014.

 

IAS-Lab RGB-D Face dataset download:

  • the dataset as compressed zip files: training set (26 people), testing set (19 people)
  • some C++ files for reading and visualizing the dataset files are present in this zip folder. 

If you use the IAS-Lab RGB-D Face dataset, please cite the following work:

G. Pitteri, M. Munaro and E. Menegatti.
"Depth-based frontal view generation for pose invariant face recognition with consumer RGB-D sensors".
In Proceedings of the 14th International Conference on Intelligent Autonomous Systems (IAS-14), Shanghai, 2016.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Copyright (c) 2016 Matteo Munaro.