Abstract
In this paper, we report our work on baseball pitch type recognition based on broadcast videos using two-stream inflated 3D convolutional neural network (I3D). To improve the state-of-the-art of research, we developed our own high-quality dataset, trained and tuned the I3D model extensively, primarily combating the problem of overfitting while still trying to improve final validation accuracy. In the end, we are able to achieve an accuracy of 53.43% ± 3.04% when oversampling and 57.10% ± 2.99% when not oversampling, which is a significant improvement over the published best result of an accuracy of 36.4% on the same six pitch type classes.
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This work is partially supported by the Undergraduate Summer Research Award program at Cleveland State University.
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Chen, R., Siegler, D., Fasko, M., Yang, S., Luo, X., Zhao, W. (2019). Baseball Pitch Type Recognition Based on Broadcast Videos. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_24
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DOI: https://doi.org/10.1007/978-981-15-1925-3_24
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