InLiDa: A 3D Lidar Dataset for People Detection and Tracking in Indoor Environments

Cristina Romero-González, Álvaro Villena, Daniel González-Medina, Jesus Martínez-Gómez, Luis Rodríguez-Ruiz, Ismael García-Varea

2017

Abstract

The objective evaluation of people detectors and trackers is essential to develop high performance and general purpose solutions to these problems. This evaluation can be easily done thanks to the use of annotated datasets, but there are some combinations of sensors and scopes that have not been extensively explored. Namely, the application of large range 3D sensors in indoor environments for people detection purposes has been sparsely studied. To fill this gap, we propose InLiDa, a dataset that consists of six different sequences acquired in two different large indoor environments. The dataset is released with a set of tools valid for its use as benchmark for people detection and tracking proposals. Also baseline results obtained with state-of-the-art techniques for people detection and tracking are presented

References

  1. Andriluka, M., Roth, S., and Schiele, B. (2008). People-tracking-by-detection and people-detectionby-tracking. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE.
  2. Benenson, R., Omran, M., Hosang, J., and Schiele, B. (2015). Ten Years of Pedestrian Detection, What Have We Learned?, pages 613-627. Springer International Publishing, Cham.
  3. Bernardin, K. and Stiefelhagen, R. (2008). Evaluating multiple object tracking performance: The clear mot metrics. EURASIP Journal on Image and Video Processing, 2008(1):1-10.
  4. Blanco, J.-L., Moreno, F.-A., and González-Jiménez, J. (2014). The málaga urban dataset: High-rate stereo and lidars in a realistic urban scenario. International Journal of Robotics Research, 33(2):207-214.
  5. Dollar, P., Wojek, C., Schiele, B., and Perona, P. (2012). Pedestrian detection: An evaluation of the state of the art. IEEE transactions on pattern analysis and machine intelligence, 34(4):743-761.
  6. Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A. (2010). The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2):303-338.
  7. Geiger, A., Lenz, P., Stiller, C., and Urtasun, R. (2013). Vision meets robotics: The kitti dataset. International Journal of Robotics Research (IJRR).
  8. Geronimo, D., Lopez, A. M., Sappa, A. D., and Graf, T. (2010). Survey of pedestrian detection for advanced driver assistance systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7):1239- 1258.
  9. Leal-Taixé, L., Milan, A., Reid, I., Roth, S., and Schindler, K. (2015). MOTChallenge 2015: Towards a benchmark for multi-target tracking. arXiv:1504.01942 [cs]. arXiv: 1504.01942.
  10. Martínez-G ómez, J., García-Varea, I., Cazorla, M., and Morell, V. (2015). Vidrilo: The visual and depth robot indoor localization with objects information dataset. The International Journal of Robotics Research, 34(14):1681-1687.
  11. Mun˜oz-Salinas, R., Aguirre, E., and García-Silvente, M. (2007). People detection and tracking using stereo vision and color. Image and Vision Computing, 25(6):995-1007.
  12. Navarro-Serment, L. E., Mertz, C., and Hebert, M. (2010). Pedestrian detection and tracking using threedimensional ladar data. The International Journal of Robotics Research, 29(12):1516-1528.
  13. Nguyen, D. T., Li, W., and Ogunbona, P. O. (2016). Human detection from images and videos: A survey. Pattern Recognition, 51:148-175.
  14. Pandey, G., McBride, J. R., and Eustice, R. M. (2011). Ford campus vision and lidar data set. The International Journal of Robotics Research, 30(13):1543-1552.
  15. Rusu, R. B. (2009). Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments. PhD thesis, Computer Science department, Technische Universitaet Muenchen, Germany.
  16. Rusu, R. B., Bradski, G., Thibaux, R., and Hsu, J. (2010). Fast 3D recognition and pose using the viewpoint feature histogram. In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pages 2155-2162. IEEE.
  17. Rusu, R. B. and Cousins, S. (2011). 3D is here: Point cloud library (PCL). In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 1-4.
  18. Smith, M., Baldwin, I., Churchill, W., Paul, R., and Newman, P. (2009). The new college vision and laser data set. The International Journal of Robotics Research, 28(5):595-599.
  19. Spinello, L. and Arras, K. O. (2011). People detection in rgb-d data. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 3838- 3843. IEEE.
  20. Wohlkinger, W. and Vincze, M. (2011). Ensemble of shape functions for 3d object classification. InRobotics and Biomimetics (ROBIO), 2011 IEEE International Conference on, pages 2987-2992. IEEE.
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Paper Citation


in Harvard Style

Romero-González C., Villena Á., González-Medina D., Martínez-Gómez J., Rodríguez-Ruiz L. and García-Varea I. (2017). InLiDa: A 3D Lidar Dataset for People Detection and Tracking in Indoor Environments . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 484-491. DOI: 10.5220/0006148704840491


in Bibtex Style

@conference{visapp17,
author={Cristina Romero-González and Álvaro Villena and Daniel González-Medina and Jesus Martínez-Gómez and Luis Rodríguez-Ruiz and Ismael García-Varea},
title={InLiDa: A 3D Lidar Dataset for People Detection and Tracking in Indoor Environments},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={484-491},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006148704840491},
isbn={978-989-758-227-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - InLiDa: A 3D Lidar Dataset for People Detection and Tracking in Indoor Environments
SN - 978-989-758-227-1
AU - Romero-González C.
AU - Villena Á.
AU - González-Medina D.
AU - Martínez-Gómez J.
AU - Rodríguez-Ruiz L.
AU - García-Varea I.
PY - 2017
SP - 484
EP - 491
DO - 10.5220/0006148704840491