Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup

• URL: http://arxiv.org/abs/2007.11876v1
• Date: Thu, 23 Jul 2020 09:31:32 GMT
• Title: Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup
• Authors: Gabriel Van Zandycke, Christophe De Vleeschouwer
• Abstract summary: This paper considers the task of detecting the ball from a single viewpoint in the challenging but common case where the ball interacts frequently with players. We propose a novel approach by formulating the problem as a segmentation task solved by an efficient CNN architecture. Our inference model can run in real time without the delay induced by a temporal analysis.
• Score: 23.17839603118139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers the task of detecting the ball from a single viewpoint in the challenging but common case where the ball interacts frequently with players while being poorly contrasted with respect to the background. We propose a novel approach by formulating the problem as a segmentation task solved by an efficient CNN architecture. To take advantage of the ball dynamics, the network is fed with a pair of consecutive images. Our inference model can run in real time without the delay induced by a temporal analysis. We also show that test-time data augmentation allows for a significant increase the detection accuracy. As an additional contribution, we publicly release the dataset on which this work is based.

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