Abstract
Perception of the environment is crucial in terms of successfully playing soccer. Especially the detection of other players improves game play skills, such as obstacle avoidance and path planning. Such information can help refine reactive behavioral strategies, and is conducive to team play capabilities. Robot detection in the RoboCup Standard Platform League is particularly challenging as the Nao robots are limited in computing resources and their appearance is predominantly white in color like the field lines.
This paper describes a vision-based multilevel approach which is integrated into the B-Human Software Framework and evaluated in terms of speed and accuracy. On the basis of color segmented images, a feed-forward neural network is trained to discriminate between robots and non-robots. The presented algorithm initially extracts image regions which potentially depict robots and prepares them for classification. Preparation comprises calculation of color histograms as well as linear interpolation in order to obtain network inputs of a specific size. After classification by the neural network, a position hypothesis is generated.
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References
Aldebaran Robotics: Nao Robot Reference Manual, Version 1.10.10, Internal Report (2010)
Bradski, G.R.: The OpenCV Library (2000), http://opencv.willowgarage.com/
Daniş, F.S., Meriçli, T., Meriçli, Ç., Akın, H.L.: Robot Detection with a Cascade of Boosted Classifiers Based on Haar-Like Features. In: Ruiz-del-Solar, J., Chown, E., Ploeger, P.G. (eds.) RoboCup 2010. LNCS (LNAI), vol. 6556, pp. 409–417. Springer, Heidelberg (2010)
Fabisch, A., Laue, T., Röfer, T.: Robot Recognition and Modeling in the RoboCup Standard Platform League. In: Proc. 5th Workshop on Humanoid Soccer Robots at Humanoids (2010)
Fasola, J., Veloso, M.M.: Real-time Object Detection using Segmented and Grayscale Images. In: IEEE International Conference on Robotics and Automation, pp. 4088–4093 (2006)
Lange, S., Riedmiller, M.: Appearance-Based Robot Discrimination Using Eigenimages. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006. LNCS (LNAI), vol. 4434, pp. 499–506. Springer, Heidelberg (2007)
Lee, Y.: Handwritten Digit Recognition Using K Nearest-Neighbor, Radial-Basis Function, and Backpropagation Neural Networks. Neural Computation 3, 440–449 (1991)
Liu, C.-L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten Digit Recognition: Benchmarking of State-of-the-art Techniques. Pattern Recognition 36(10), 2271–2285 (2003)
Mayer, G., Kaufmann, U., Kraetzschmar, G.K., Palm, G.: Neural Robot Detection in RoboCup. In: Wermter, S., Palm, G., Elshaw, M. (eds.) Biomimetic Neural Learning for Intelligent Robots. LNCS (LNAI), vol. 3575, pp. 349–361. Springer, Heidelberg (2005)
Röfer, T., Laue, T., Müller, J., Bösche, O., Burchardt, A., Damrose, E., Gillmann, K., Graf, C., de Haas, T.J., Härtl, A., Rieskamp, A., Schreck, A., Sieverdingbeck, I., Worch, J.H.: B-Human Team Report and Code Release 2009 (2009)
Ruiz-del-Solar, J., Verschae, R., Arenas, M., Loncomilla, P.: Play ball! fast and accurate multiclass visual detection of robots and its application to behavior recognition. IEEE Robotics Automation Magazine 17(4), 43–53 (2010)
Sony Corporation: AIBO (1999), http://support.sony-europe.com/aibo/
Viola, P.A., Jones, M.J.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: CVPR, vol. 1, pp. 511–518 (2001)
Welch, G., Bishop, G.: An Introduction to the Kalman Filter. Tech. Rep. 95-041, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (1995)
Wilking, D., Röfer, T.: Realtime Object Recognition Using Decision Tree Learning. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 556–563. Springer, Heidelberg (2005)
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Metzler, S., Nieuwenhuisen, M., Behnke, S. (2012). Learning Visual Obstacle Detection Using Color Histogram Features. In: Röfer, T., Mayer, N.M., Savage, J., Saranlı, U. (eds) RoboCup 2011: Robot Soccer World Cup XV. RoboCup 2011. Lecture Notes in Computer Science(), vol 7416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32060-6_13
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DOI: https://doi.org/10.1007/978-3-642-32060-6_13
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