Field position estimation in soccer videos using convolutional neural network-based image features

Genki Suzuki; Sho Takahashi; Takahiro Ogawa; Miki Haseyama

doi:10.1117/12.2521569

22 March 2019 Field position estimation in soccer videos using convolutional neural network-based image features

Genki Suzuki, Sho Takahashi, Takahiro Ogawa, Miki Haseyama

Proceedings Volume 11049, International Workshop on Advanced Image Technology (IWAIT) 2019; 110490Y (2019) https://doi.org/10.1117/12.2521569
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

This paper presents a novel estimation method of field positions in soccer videos using Convolutional Neural Network (CNN)-based image features. CNN-based features have been well known to be eﬀective for tasks in machine learning. Therefore, the proposed method adopts CNN-based image features. By using these image features, the proposed method enables accurate estimation of soccer field positions than handcrafted features, i.e., Hough transform-based features. We show the eﬀectiveness of our method via experiment results using actual soccer videos.

Citation Download Citation

Genki Suzuki, Sho Takahashi, Takahiro Ogawa, and Miki Haseyama "Field position estimation in soccer videos using convolutional neural network-based image features", Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110490Y (22 March 2019); https://doi.org/10.1117/12.2521569

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