Cited By
View all- Biddulph AHouliston TMendes AChalup S(2018)Comparing Computing Platforms for Deep Learning on a Humanoid RobotNeural Information Processing10.1007/978-3-030-04239-4_11(120-131)Online publication date: 13-Dec-2018
A study on detecting three-dimensional balls using boosted classifiers
Authors: 
Mitchell Metcalfe, Brendan Annable, Monica Olejniczak, Stephan K. ChalupAuthors Info & Claims
Article No.: 71, Pages 1 - 6
Abstract
Many recent approaches to ball detection in robot soccer reduce the task to edge-based circle detection, or training a classifier to detect specific balls with known colour or surface texture. In the present work, a more general approach to ball detection is investigated, where spherical 3D objects must be detected under unknown lighting, colouring and texturing. Pilot experiments applied techniques stemming from the face detection literature, namely boosted-classifiers using extended Haar features, and Local Binary Patterns (LBPs) as features. Disk-like objects were included as negative samples in the training set in order to produce a detector that does not misclassify circular, disk-like objects as 3-dimensional balls. The resulting classifiers were able to detect homogeneously or moderately textured balls while robust detection of balls with unknown strong patterns still remains a challenge.
References
[1]
B. Annable, D. Budden, S. Calland, S. K. Chalup, S. K. Fenn, M. Flannery, J. Fountain, R. A. King, A. Mendes, and M. Metcalfe. The NUbots team description paper 2013, RoboCup Humanoid League, 2013.
[2]
F. Christiane. WordNet: An Electronic Lexical Database. Bradford Books, 1998.
[3]
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In IEEE Conference on Computer Vision and Pattern Recognition, 2009 (CVPR 2009), pages 248--255, 2009.
[4]
M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381--395, 1981.
[5]
R. Gross and V. Brajovic. An image preprocessing algorithm for illumination invariant face recognition. In Audio-and Video-Based Biometric Person Authentication - 4th International Conference, AVBPA 2003 Guildford, UK, June 9-11, 2003 Proceedings, volume 2688 of Lecture Notes in Computer Science, pages 10--18, Berlin, 2003. Springer-Verlag.
[6]
N. Henderson, R. King, and S. Chalup. An automated colour calibration system using multivariate gaussian mixtures to segment hsi colour space. In J. Kim and R. Mahony, editors, Proceedings of the 2008 Australasian Conference on Robotics and Automation (ACRA 2008). ARAA (on-line), 2008.
[7]
H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, E. Osawa, and H. Matsubara. Robocup. a challenge problem for ai. AI Magazine, 18(1):73--85, 1997.
[8]
X. Li, H. Lu, D. Xiong, H. Zhang, and Z. Zheng. A survey on visual perception for RoboCup MSL soccer robots. International Journal of Advanced Robotic Systems, 10: 110--120, 2013.
[9]
S. Liao, X. Zhu, Z. Lei, L. Zhang, and S. Z. Li. Learning multi-scale block local binary patterns for face recognition. In Advances in Biometrics - International Conference, ICB 2007, Seoul, Korea, August 27-29, 2007. Proceedings, volume 4642 of Lecture Notes in Computer Science, pages 828--837. Springer, 2007.
[10]
R. Lienhart and J. Maydt. An extended set of haar-like features for rapid object detection. In International Conference on Image Processing, 2002, volume 1, pages 900--903. IEEE, 2002.
[11]
A. Masselli, R. Hanten, and A. Zell. Robust real-time detection of multiple balls on a mobile robot. In European Conference on Mobile Robots (ECMR 2013), pages 355--360, September 2013.
[12]
S. Mitri, K. Pervölz, H. Surmann, and A. Nüchter. Fast color-independent ball detection for mobile robots. In Proceedings of the IEEE Computer Society International Conference Mechatronics and Robotics, pages 900--905, 2004.
[13]
F. F. Mueller, D. Edge, F. Vetere, M. R. Gibbs, S. Agamanolis, B. Bongers, and J. G. Sheridan. Designing sports: A framework for exertion games. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '11, pages 2651--2660, New York, NY, USA, 2011. ACM.
[14]
C. L. Murch and S. K. Chalup. Combining edge detection and colour segmentation in the four-legged league. In Proceedings of the 2004 Australasian Conference on Robotics & Automation (ACRA '2004). ARAA (on-line), 2004.
[15]
P. Nillius, J. Sullivan, and A. Argyros. Shading models for illumination and reflectance invariant shape detectors. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2014), pages 1--8, Los Alamitos, CA, USA, 2008. IEEE.
[16]
V. Patraucean, P. Gurdjos, and R. G. von Gioi. A parameterless line segment and elliptical arc detector with enhanced ellipse fitting. In A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, and C. Schmid, editors, Computer Vision - ECCV 2012, 12th European Conference on Computer Vision Florence, Italy, October 7-13, 2012 Proceedings, Part II, volume 7573 of Lecture Notes in Computer Science (LNCS), pages 572--585, 2012.
[17]
H. Schulz, H. Strasdat, and S. Behnke. A ball is not just orange: Using color and luminance to classify regions of interest. In Proceedings of the Second Workshop on Humanoid Soccer Robots, Pittsburgh, PA, USA, 2007.
[18]
A. Treptow and A. Zell. Real-time object tracking for soccer-robots without color information. Robotics and Autonomous Systems, 48(1):41--48, 2004.
[19]
P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), volume 1, pages 511--518. IEEE, 2001.
[20]
P. Viola and M. Jones. Robust real-time object detection. International Journal of Computer Vision, 57(2):137--154, 2004.
[21]
H. Zhang, H. Lu, P. Dong, D. Xiong, and Z. Zheng. A novel generic ball recognition algorithm based on omnidirectional vision for soccer robots. International Journal of Advanced Robotic Systems, 10(388), 2013.
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Published In
February 2016
654 pages
ISBN:9781450340427
DOI:10.1145/2843043
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Published: 01 February 2016
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View all- Biddulph AHouliston TMendes AChalup S(2018)Comparing Computing Platforms for Deep Learning on a Humanoid RobotNeural Information Processing10.1007/978-3-030-04239-4_11(120-131)Online publication date: 13-Dec-2018
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