Skip to main content
Springer Nature Link
Log in

TORSO-21 Dataset: Typical Objects in RoboCup Soccer 2021

  • Conference paper
  • First Online:
RoboCup 2021: Robot World Cup XXIV (RoboCup 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13132))

Included in the following conference series:

Abstract

We present a dataset specifically designed to be used as a benchmark to compare vision systems in the RoboCup Humanoid Soccer domain. The dataset is composed of a collection of images taken in various real-world locations as well as a collection of simulated images. It enables comparing vision approaches with a meaningful and expressive metric. The contributions of this paper consist of providing a comprehensive and annotated dataset, an overview of the recent approaches to vision in RoboCup, methods to generate vision training data in a simulated environment, and an approach to increase the variety of a dataset by automatically selecting a diverse set of images from a larger pool. Additionally, we provide a baseline of YOLOv4 and YOLOv4-tiny on this dataset.

All authors contributed equally.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://humanoid.robocup.org/hl-2021/v-hsc/ (last accessed: 2021/06/14)

  2. 2.

    https://github.com/noctrog/conv-vae (last accessed: 2021/06/14)

References

  1. Albani, D., Youssef, A., Suriani, V., Nardi, D., Bloisi, D.D.: A deep learning approach for object recognition with NAO soccer robots. In: Behnke, S., Sheh, R., Sarıel, S., Lee, D.D. (eds.) RoboCup 2016. LNCS (LNAI), vol. 9776, pp. 392–403. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68792-6_33

    Chapter  Google Scholar 

  2. Asada, M., von Stryk, O.: Scientific and technological challenges in robocup. Ann. Rev. Control Robot. Auton. Syst. 3, 441–471 (2020)

    Article  Google Scholar 

  3. Barry, D., Shah, M., Keijsers, M., Khan, H., Hopman, B.: xYOLO: a model for real-time object detection in humanoid soccer on low-end hardware. In: International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1–6. IEEE (2019)

    Google Scholar 

  4. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  5. Cruz, N., Leiva, F., Ruiz-del-Solar, J.: Deep learning applied to humanoid soccer robotics: playing without using any color information. Auton. Robot. 45(3), 335–350 (2021). https://doi.org/10.1007/s10514-021-09966-9

    Article  Google Scholar 

  6. Cruz, N., Lobos-Tsunekawa, K., Ruiz-del-Solar, J.: Using convolutional neural networks in robots with limited computational resources: detecting NAO robots while playing soccer. In: Akiyama, H., Obst, O., Sammut, C., Tonidandel, F. (eds.) RoboCup 2017. LNCS (LNAI), vol. 11175, pp. 19–30. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00308-1_2

    Chapter  Google Scholar 

  7. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  8. van Dijk, S.G., Scheunemann, M.M.: Deep learning for semantic segmentation on minimal hardware. In: Holz, D., Genter, K., Saad, M., von Stryk, O. (eds.) RoboCup 2018. LNCS (LNAI), vol. 11374, pp. 349–361. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27544-0_29

    Chapter  Google Scholar 

  9. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010). https://doi.org/10.1007/s11263-009-0275-4

    Article  Google Scholar 

  10. Farazi, H., Behnke, S.: Real-time visual tracking and identification for a team of homogeneous humanoid robots. In: Behnke, S., Sheh, R., Sarıel, S., Lee, D.D. (eds.) RoboCup 2016. LNCS (LNAI), vol. 9776, pp. 230–242. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68792-6_19

    Chapter  Google Scholar 

  11. Farazi, H., et al.: NimbRo robots winning RoboCup 2018 humanoid AdultSize soccer competitions. In: Holz, D., Genter, K., Saad, M., von Stryk, O. (eds.) RoboCup 2018. LNCS (LNAI), vol. 11374, pp. 436–449. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27544-0_36

    Chapter  Google Scholar 

  12. Felbinger, G.C., Göttsch, P., Loth, P., Peters, L., Wege, F.: Designing convolutional neural networks using a genetic approach for ball detection. In: Holz, D., Genter, K., Saad, M., von Stryk, O. (eds.) RoboCup 2018. LNCS (LNAI), vol. 11374, pp. 150–161. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27544-0_12

    Chapter  Google Scholar 

  13. Fiedler, N., Bestmann, M., Hendrich, N.: ImageTagger: an open source online platform for collaborative image labeling. In: Holz, D., Genter, K., Saad, M., von Stryk, O. (eds.) RoboCup 2018. LNCS (LNAI), vol. 11374, pp. 162–169. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27544-0_13

    Chapter  Google Scholar 

  14. Fiedler, N., Brandt, H., Gutsche, J., Vahl, F., Hagge, J., Bestmann, M.: An open source vision pipeline approach for RoboCup humanoid soccer. In: Chalup, S., Niemueller, T., Suthakorn, J., Williams, M.-A. (eds.) RoboCup 2019. LNCS (LNAI), vol. 11531, pp. 376–386. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35699-6_29

    Chapter  Google Scholar 

  15. Gabel, A., Heuer, T., Schiering, I., Gerndt, R.: Jetson, where is the ball? Using neural networks for ball detection at RoboCup 2017. In: Holz, D., Genter, K., Saad, M., von Stryk, O. (eds.) RoboCup 2018. LNCS (LNAI), vol. 11374, pp. 181–192. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27544-0_15

    Chapter  Google Scholar 

  16. Gondry, L., et al.: Rhoban football club: RoboCup humanoid KidSize 2019 champion team paper. In: Chalup, S., Niemueller, T., Suthakorn, J., Williams, M.-A. (eds.) RoboCup 2019. LNCS (LNAI), vol. 11531, pp. 491–503. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35699-6_40

    Chapter  Google Scholar 

  17. Hess, T., Mundt, M., Weis, T., Ramesh, V.: Large-scale stochastic scene generation and semantic annotation for deep convolutional neural network training in the RoboCup SPL. In: Akiyama, H., Obst, O., Sammut, C., Tonidandel, F. (eds.) RoboCup 2017. LNCS (LNAI), vol. 11175, pp. 33–44. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00308-1_3

    Chapter  Google Scholar 

  18. Javadi, M., Azar, S.M., Azami, S., Ghidary, S.S., Sadeghnejad, S., Baltes, J.: Humanoid robot detection using deep learning: a speed-accuracy tradeoff. In: Akiyama, H., Obst, O., Sammut, C., Tonidandel, F. (eds.) RoboCup 2017. LNCS (LNAI), vol. 11175, pp. 338–349. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00308-1_28

    Chapter  Google Scholar 

  19. Kukleva, A., Khan, M.A., Farazi, H., Behnke, S.: Utilizing temporal information in deep convolutional network for efficient soccer ball detection and tracking. In: Chalup, S., Niemueller, T., Suthakorn, J., Williams, M.-A. (eds.) RoboCup 2019. LNCS (LNAI), vol. 11531, pp. 112–125. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35699-6_9

    Chapter  Google Scholar 

  20. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  21. Menashe, J., et al.: Fast and precise black and white ball detection for RoboCup soccer. In: Akiyama, H., Obst, O., Sammut, C., Tonidandel, F. (eds.) RoboCup 2017. LNCS (LNAI), vol. 11175, pp. 45–58. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00308-1_4

    Chapter  Google Scholar 

  22. Michel, O.: Cyberbotics ltd. webots\(^{{\rm TM}}\): professional mobile robot simulation. Int. J. Adv. Robot. Syst. 1(1), 5 (2004)

    Google Scholar 

  23. Poppinga, B., Laue, T.: JET-Net: real-time object detection for mobile robots. In: Chalup, S., Niemueller, T., Suthakorn, J., Williams, M.-A. (eds.) RoboCup 2019. LNCS (LNAI), vol. 11531, pp. 227–240. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35699-6_18

    Chapter  Google Scholar 

  24. Schnekenburger, F., Scharffenberg, M., Wülker, M., Hochberg, U., Dorer, K.: Detection and localization of features on a soccer field with feedforward fully convolutional neural networks (FCNN) for the adult-size humanoid robot sweaty. In: Proceedings of the 12th Workshop on Humanoid Soccer Robots, IEEE-RAS International Conference on Humanoid Robots, Birmingham. Sn (2017)

    Google Scholar 

  25. Speck, D., Barros, P., Weber, C., Wermter, S.: Ball localization for Robocup soccer using convolutional neural networks. In: Behnke, S., Sheh, R., Sarıel, S., Lee, D.D. (eds.) RoboCup 2016. LNCS (LNAI), vol. 9776, pp. 19–30. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68792-6_2

    Chapter  Google Scholar 

  26. Speck, D., Bestmann, M., Barros, P.: Towards real-time ball localization using CNNs. In: Holz, D., Genter, K., Saad, M., von Stryk, O. (eds.) RoboCup 2018. LNCS (LNAI), vol. 11374, pp. 337–348. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27544-0_28

    Chapter  Google Scholar 

  27. Szemenyei, M., Estivill-Castro, V.: Real-time scene understanding using deep neural networks for RoboCup SPL. In: Holz, D., Genter, K., Saad, M., von Stryk, O. (eds.) RoboCup 2018. LNCS (LNAI), vol. 11374, pp. 96–108. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27544-0_8

    Chapter  Google Scholar 

  28. Szemenyei, M., Estivill-Castro, V.: ROBO: robust, fully neural object detection for robot soccer. In: Chalup, S., Niemueller, T., Suthakorn, J., Williams, M.-A. (eds.) RoboCup 2019. LNCS (LNAI), vol. 11531, pp. 309–322. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35699-6_24

    Chapter  Google Scholar 

  29. Szemenyei, M., Estivill-Castro, V.: Fully neural object detection solutions for robot soccer. Neural Comput. Appl. 1–14 (2021). https://doi.org/10.1007/s00521-021-05972-1

  30. Teimouri, M., Delavaran, M.H., Rezaei, M.: A real-time ball detection approach using convolutional neural networks. In: Chalup, S., Niemueller, T., Suthakorn, J., Williams, M.-A. (eds.) RoboCup 2019. LNCS (LNAI), vol. 11531, pp. 323–336. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35699-6_25

    Chapter  Google Scholar 

  31. Tsipras, D., Santurkar, S., Engstrom, L., Ilyas, A., Madry, A.: From ImageNet to image classification: contextualizing progress on benchmarks. In: International Conference on Machine Learning, pp. 9625–9635. PMLR (2020)

    Google Scholar 

  32. Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)

Download references

Acknowledgments

Thanks to all individuals and teams that provided data and labels or helped to develop and host the ImageTagger.

This research was partially funded by the Ministry of Science, Research and Equalities of Hamburg as well as the German Research Foundation (DFG) and the National Science Foundation of China (NSFC) in project Crossmodal Learning, TRR-169.

Author information

Authors and Affiliations

  1. Hamburg Bit-Bots, Department of Informatics, Universität Hamburg, Vogt-Kölln-Straße 30, 22527, Hamburg, Germany

    Marc Bestmann, Timon Engelke, Niklas Fiedler, Jasper Güldenstein, Jan Gutsche, Jonas Hagge & Florian Vahl

Authors
  1. Marc Bestmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Timon Engelke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Niklas Fiedler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jasper Güldenstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jan Gutsche
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jonas Hagge
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Florian Vahl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Florian Vahl .

Editor information

Editors and Affiliations

  1. LAAS-CNRS/ANITI, Toulouse, France

    Rachid Alami

  2. The University of Texas at Austin, Austin, TX, USA

    Joydeep Biswas

  3. University of Washington, Seattle, WA, USA

    Maya Cakmak

  4. Western Sydney University, Penrith, NSW, Australia

    Oliver Obst

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bestmann, M. et al. (2022). TORSO-21 Dataset: Typical Objects in RoboCup Soccer 2021. In: Alami, R., Biswas, J., Cakmak, M., Obst, O. (eds) RoboCup 2021: Robot World Cup XXIV. RoboCup 2021. Lecture Notes in Computer Science(), vol 13132. Springer, Cham. https://doi.org/10.1007/978-3-030-98682-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-98682-7_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98681-0

  • Online ISBN: 978-3-030-98682-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics