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Open Access Convolutional neural networks for the analysis of broadcasted tennis games

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The analysis of complex structured data like video has been a long-standing challenge for computer vision algorithms. Innovative deep learning architectures like Convolutional Neural Networks (CNNs), however are demonstrating remarkable performance in challenging image and video understanding tasks. In this work we propose a architecture for the automated detection of scored points during tennis matches. We explore two approaches based on CNNs for the analysis of video streams of broadcasted tennis games. We first explore the two-stream approach, which involves extracting features related to either pixel intensity values via the analysis of grayscale frames or the encoding of motion related information via optical flow. However, we explore the case of using higher order 3D CNN for simultaneously encoding both spatial and temporal correlations. Furthermore, we explore the late fusion of the individual stream in order to extract and encode both structural and motion spatio-temporal dynamics. We validate the merits of the proposed scheme using a novel manually annotated dataset created from publically available videos.
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Document Type: Research Article

Publication date: January 1, 2018

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

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