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Annals of Computer Science and Information Systems, Volume 17

Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems

Soccer object motion recognition based on 3D convolutional neural networks

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DOI: http://dx.doi.org/10.15439/2018F48

Citation: Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 17, pages 129–134 ()

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Abstract. Due to the development of video understanding and big data analysis research field using deep learning technique, intelligent machines have replaced the tasks that people performed in the past in various fields such as traffic, surveillance, and security area. In the sports field, especially in soccer games, it is also attempting quantitative analysis of players and games through deep learning or big data analysis technique. However, because of the nature of soccer analysis, it is still difficult to make sophisticated automatic analysis due to technical limitations. In this paper, we propose a deep learning based motion recognition technique which is the basis of high level automatic soccer analysis. For sophisticated motion recognition, we maximize recognition accuracy by sequentially processing the data in three steps: data acquisition, data augmentation, and 3D CNN based motion classifier learning. As can be seen from the experimental results, the proposed method guarantees real-time speed performance and satisfactory accuracy performance.

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