Mitchell Metcalfe
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.
- 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.Google Scholar
- F. Christiane. WordNet: An Electronic Lexical Database. Bradford Books, 1998.Google Scholar
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google Scholar
- A. Treptow and A. Zell. Real-time object tracking for soccer-robots without color information. Robotics and Autonomous Systems, 48(1):41--48, 2004.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- P. Viola and M. Jones. Robust real-time object detection. International Journal of Computer Vision, 57(2):137--154, 2004. Google ScholarDigital Library
- 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.Google Scholar
Recommendations
Framework for research on detection classifiers
SCCG '08: Proceedings of the 24th Spring Conference on Computer GraphicsDetection of patterns in images with classifiers is currently one of the most important research topics in computer vision. Many practical applications such as face detection exist and recent work even suggests that any specialized detectors (e.g. ...
AdaBoost classifiers for pecan defect classification
Highlights The performance of AdaBoost algorithms were compared with support vector machine and Bayesian classifiers for pecan defect classification. AdaBoost classifiers took least time and gave best classification accuracy. AdaBoost classifiers ...
Scene categorization using boosted back-propagation neural networks
PCM'10: Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part IScene categorization plays an important role in computer vision, image content understanding, and image retrieval. In this paper, back-propagation neural network (BPN) is served as the basic classifier for multi-class scene/image categorization. Four ...
Comments