Teimouri, M, Delavaran, MH and Rezaei, M orcid.org/0000-0003-3892-421X (2019) A Real-Time Ball Detection Approach Using Convolutional Neural Networks. In: Lecture Notes in Computer Science. RoboCup 2019: Robot World Cup XXIII, 02-08 Jul 2019, Sydney, Australia. Springer , pp. 323-336. ISBN 978-3-030-35698-9
Abstract
Ball detection is one of the most important tasks in the context of soccer-playing robots. The ball is a small moving object which can be blurred and occluded in many situations. Several neural network based methods with different architectures are proposed to deal with the ball detection. However, they are either neglecting to consider the computationally low resources of humanoid robots or highly depend on manually-tuned heuristic methods to extract the ball candidates. In this paper, we propose a new ball detection method for low-cost humanoid robots that can detect most soccer balls with a high accuracy rate of up to 97.17%. The proposed method is divided into two steps. First, some coarse regions that may contain a full ball are extracted using an iterative method employing an efficient integral image based feature. Then they are fed to a light-weight convolutional neural network to finalize the bounding box of a ball. We have evaluated the proposed approach using a comprehensive dataset and the experimental results show the efficiency of our method.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2019. This is an author produced version of a conference paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Ball detection; Convolutional neural networks; Humanoid robot; RoboCup |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Sep 2020 15:32 |
Last Modified: | 06 Sep 2020 04:32 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-35699-6_25 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164959 |