publication . Preprint . 2017

Machine Learning for RealisticBall Detection in RoboCup SPL

Bloisi, Domenico; Del Duchetto, Francesco; Manoni, Tiziano; Suriani, Vincenzo;
Open Access English
  • Published: 12 Jul 2017
Abstract
In this technical report, we describe the use of a machine learning approach for detecting the realistic black and white ball currently in use in the RoboCup Standard Platform League. Our aim is to provide a ready-to-use software module that can be useful for the RoboCup SPL community. To this end, the approach is integrated within the official B-Human code release 2016. The complete code for the approach presented in this work can be downloaded from the SPQR Team homepage at http://spqr.diag.uniroma1.it and from the SPQR Team GitHub repository at https://github.com/SPQRTeam/SPQRBallPerceptor. The approach has been tested in multiple environments, both indoor an...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
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2 Background and Related Work 2 2.1 OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3 Method 3 3.1 Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2 Patches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2.1 Patches predictors . . . . . . . . . . . . . . . . . . . 5

4 Training 5 4.1 Create the positive training data . . . . . . . . . . . . . . . 6 4.2 Create the negative training data . . . . . . . . . . . . . . . 6 4.3 Train the classifier . . . . . . . . . . . . . . . . . . . . . . . 7

[1] D Albani, A Youssef, V Suriani, D Nardi, and DD Bloisi. A deep learning approach for object recognition with nao soccer robots. In Robocup Symposium: Poster presentation, 2016.

[2] Luca Iocchi. Robust color segmentation through adaptive color distribution transformation. In Robot Soccer World Cup, pages 287-295. Springer, 2006.

[3] Andrea Pennisi, Domenico D. Bloisi, Luca Iocchi, and Daniele Nardi. Ground Truth Acquisition of Humanoid Soccer Robot Behaviour, pages 560-567. 2014.

Abstract
In this technical report, we describe the use of a machine learning approach for detecting the realistic black and white ball currently in use in the RoboCup Standard Platform League. Our aim is to provide a ready-to-use software module that can be useful for the RoboCup SPL community. To this end, the approach is integrated within the official B-Human code release 2016. The complete code for the approach presented in this work can be downloaded from the SPQR Team homepage at http://spqr.diag.uniroma1.it and from the SPQR Team GitHub repository at https://github.com/SPQRTeam/SPQRBallPerceptor. The approach has been tested in multiple environments, both indoor an...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download from

2 Background and Related Work 2 2.1 OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3 Method 3 3.1 Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2 Patches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2.1 Patches predictors . . . . . . . . . . . . . . . . . . . 5

4 Training 5 4.1 Create the positive training data . . . . . . . . . . . . . . . 6 4.2 Create the negative training data . . . . . . . . . . . . . . . 6 4.3 Train the classifier . . . . . . . . . . . . . . . . . . . . . . . 7

[1] D Albani, A Youssef, V Suriani, D Nardi, and DD Bloisi. A deep learning approach for object recognition with nao soccer robots. In Robocup Symposium: Poster presentation, 2016.

[2] Luca Iocchi. Robust color segmentation through adaptive color distribution transformation. In Robot Soccer World Cup, pages 287-295. Springer, 2006.

[3] Andrea Pennisi, Domenico D. Bloisi, Luca Iocchi, and Daniele Nardi. Ground Truth Acquisition of Humanoid Soccer Robot Behaviour, pages 560-567. 2014.

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