Skip to main content
SpringerLink
Log in

Evaluating the Performance of Two Computer Vision Techniques for a Mobile Humanoid Agent Acting at RoboCup KidSized Soccer League

  • Conference paper
  • First Online:
Robotics (SBR 2016, LARS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 619))

Included in the following conference series:

  • 1040 Accesses

Abstract

A humanoid robot capable of playing soccer needs to identify several objects in the soccer field in order to play soccer. The robot has to be able to recognize the ball, teammates and opponents, inferring information such as their distance and estimated location. In order to achieve this key requisite, this paper analyzes two descriptor algorithms, HAAR and HOG, so that one of them can be used for recognizing humanoid robots with less false positives alarms and with best frame per second rate. They were used with their respective classical classifiers, AdaBoost and SVM. As many different robots are available in RoboCup domain, the descriptor needs to describe features in a way that they can be distinguished from the background at the same time the classification has to have a good generalization capability. Although some limitations appeared in tests, the results were beyond expectations. Given the results, the chosen descriptor should be able to identify a mainly white-ball, which is clearly a simpler object. The results for ball detection were also quite interesting.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Perico, D., Silva, I., Vilao, C., Homem, T., Destro, R., Tonidandel, F., Bianchi, R.: Hardware, software aspects of the design, assembly of a new humanoid robot for robocup soccer. In: 2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol (SBR LARS Robocontrol), pp. 73–78, October 2014. doi:10.1109/SBR.LARS.Robocontrol.2014.39

  2. Ha, I., Tamura, Y., Asama, H., Han, J., Hong, D.W.: Development of open humanoid platform DARwin-OP, pp. 2178–2181 (2011)

    Google Scholar 

  3. Nike Website (2015). http://store.nike.com/gb/en_gb/pw/football-balls/896Zof3. Acessed July 2015

  4. Lienhart, R., Maydt, J.: An extended set of Haar like features for rapid object detection. In: International Conference on Image Processing (2002). doi:10.1109/ICIP.2002.1038171

  5. Gerónimo, D., López, A., Ponsa, D., Sappa, A.D.: Haar wavelets and edge orientation histograms for on-board pedestrian detection. In: Proceedings of the 3rd Iberian Conference on Pattern Recognition and Image Analysis, Part I, pp. 418–425 (2007). doi:10.1007/978-3-540-72847-4_54

  6. Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Ninth IEEE International Conference on Computer Vision (2003). doi:10.1109/ICCV.2003.1238422

  7. Wei, Y., Tian, Q., Guo, T.: An improved pedestrian detection algorithm integrating Haar-like features and HOG descriptors. Adv. Mech. Eng. (2013). doi:10.1155/546206

  8. Schiele, B., Andriluka, M., Majer, N., Roth, S., Wojek, C.: Visual people detection - diferent models, comparison and discussion. In: Proceedings of the IEEE ICRA Workshop on People Detection and Tracking (2009)

    Google Scholar 

  9. Brehar, R., Nedevschi, S.: A comparative study of pedestrian detection methods using classical Haar and HOG features versus bag of words model computed from Haar and HOG features. Intell. Comput. Commun. Process. (ICCP) (2011). doi:10.1109/ICCP.2011.6047884

  10. Negri, P., Clady, X., Prevost, L.: Benchmarking Haar and histograms of oriented gradients features applied to vehicle detection. In: Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, Robotics and Automation, ICINCO, Angers, France, 9–12 May 2007, vol. 1, pp. 359–364 (2007)

    Google Scholar 

  11. Paisitkriangkrai, S., Shen, C., Zhang, J.: Face detection with effective feature extraction. CoRR, abs/1009.5758 (2010)

    Google Scholar 

  12. Ju, Y., Zhang, H., Xue, Y.: Research of feature selection and comparision in adaboost based object detection system. J. Comput. Inf. Syst. 9(22), 8947–8954 (2013)

    Google Scholar 

  13. Zhang, L., Chu, R., Xiang, S., Liao, C., Li, S.Z.: Face detection based on multi-block LBP representation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 11–18. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74549-5_2

    Chapter  Google Scholar 

  14. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  15. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 511–518 (2001)

    Google Scholar 

  16. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting (1997). http://portal.acm.org/citation.cfm?id=261540.261549

  17. Vilão, C.J., Silva, I.J., Perico, D.H., Homem, T.P.D., Tonidandel, F., da Costa Bianchi, R.A.: A single camera vision system for a humanoid robot. In: Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol, pp. 181–186 (2014). doi:10.1109/SBR.LARS.Robocontrol.2014.51

  18. Vapnik, V.N., Chervonenkis, A.Y.: Support-vector networks. Mach. Learn. 20(3), 273 (1995). doi:10.1007/BF00994018

    MATH  Google Scholar 

  19. Estivill-Castro, V., Radev, J.: Humanoids learning who are teammates and who are opponents. In: The 8th Workshop on Humanoid Soccer Robots 13th at IEEE-RAS International Conference on Humanoid Robots (2013)

    Google Scholar 

  20. Nao: Aldebaran Robotics. SAS (Limited Company) (2015). http://www.active8robots.com/wp-content/uploads/File-1400771269.pdf. Accessed 2 June 2015

  21. Kim, J., Shin, H.: Algorithm & SoC Design for Automotive Vision Systems: For Smart Safe Driving System. Springer, Dordrecht (2014)

    Book  Google Scholar 

  22. Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: International Conference on Computer Vision (1998)

    Google Scholar 

  23. Lienhart, R., Kuranov, A., Pisarevsky, V.: An empirical analysis of boosting algorithms for rapid objects with an extended set of Haar-like features. Intel Technical report MRL-TR-July02-01 (1998). http://www.lienhart.de/Publications/DAGM2003.pdf

  24. Haykin, S.: Neural Networks: A Comprehensive Fundation, 2nd edn. Prentice Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  25. Osuna, E., Hearst, M.A., Dumais, S.T., Platt, J., Schlkopf, B.: Applying SVMS to face detection. IEEE Computer Society (1999)

    Google Scholar 

  26. Dumais, S.T., Hearst, M.A., Osuna, E., Platt, J., Schlkopf, B.: Using SVMS for text categorization. IEEE Computer Society (1999)

    Google Scholar 

  27. Lorena, A.C., Carvalho, A.C.P.d.L.F.d.C.: Introdução as máquinas de vetores suporte. Relatórios Técnicos do ICMC: USP, São Carlos, ISSN 0103-2569 (2003)

    Google Scholar 

  28. OpenCV: Open source computer vision library (2014). http://www.opencv.org. Accessed 20 May 2014

  29. Dimashova, M.: Can not get new positive sample. http://answers.opencv.org/question/4368/traincascade-error-bad-argument-can-not-get-new-positive-sample-the-most-possible-reason-is-insufficient-count-of-samples-in-given-vec-file/#4474. Accessed 10 Apr 2014

  30. van Dijk, S., Noakes, D., Barry, D., Polani, D.: Bold hearts team description robocup 2014 kid size, April 2014. http://homepages.stca.herts.ac.uk/epics/boldhearts/

  31. Bold hearts - Robocup 2015 qualification - kid size humanoid league (2015). https://youtu.be/pzYHAp7b7sY. Acessed Dec 2015

  32. Le, Q.V.: Building high-level features using large scale unsupervised learning. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8595–8598. IEEE (2013)

    Google Scholar 

Download references

Acknowledgment

The authors would like to thank the University Center of FEI for the available resources, and the total cooperation of the researchers involved in this project, including the Robotics and Artificial Intelligence Laboratory that supports this project. The authors would also like to thank to Brazilian research fomentation agencies, FAPESP, CNPQ and CAPES, for the scholarships provided.

Author information

Authors and Affiliations

  1. Electrical Engineering Department, FEI University Center, Sao Bernardo do Campo, Sao Paulo, Brazil

    Claudio O Vilão Jr., Vinicius Nicassio Ferreira & Reinaldo A. C. Bianchi

  2. Brazil Center of Engineering, Modelling and Applied Social Sciences, Federal University of ABC, Santo Andre, Sao Paulo, Brazil

    Luiz Antonio Celiberto Jr.

Authors
  1. Claudio O Vilão Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vinicius Nicassio Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luiz Antonio Celiberto Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Reinaldo A. C. Bianchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claudio O Vilão Jr. .

Editor information

Editors and Affiliations

  1. SSC - Depto. de Sistemas de Computação, USP - University of Sao Paulo (São Carlos), São Carlos, São Paulo, Brazil

    Fernando Santos Osório

  2. UFU – Universidade Federal de Uberlândia , Uberlândia, Minas Gerais, Brazil

    Rogério Sales Gonçalves

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Vilão, C.O., Ferreira, V.N., Celiberto, L.A., Bianchi, R.A.C. (2016). Evaluating the Performance of Two Computer Vision Techniques for a Mobile Humanoid Agent Acting at RoboCup KidSized Soccer League. In: Santos Osório, F., Sales Gonçalves, R. (eds) Robotics. SBR LARS 2016 2016. Communications in Computer and Information Science, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-319-47247-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47247-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47246-1

  • Online ISBN: 978-3-319-47247-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation