Abstract
One of the common faults in volleyball is double contact while setting the ball for a spike. It is hard to detect this fault by the players. Even the referees sometimes find it difficult to observe. In this work, we propose an automatic double contact fault detection approach using a single camera in outdoor volleyball video. The video is first analyzed to detect and track the ball; the bounding boxes are then processed to extract a deep Spatio-temporal representation using a state-of-the-art 3D-convolution-based neural network, which is finally fed to a multilayer perceptron for classification. To the best of our knowledge, this is the first work on volleyball double-contact detection. The proposed framework achieves an average accuracy of 77.16% on 5-fold cross-validation. The framework is useful for players during training and for referees as a decision-support tool.
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Acknowledgements
This work is supported by the grant received from DST, Govt. of India for the Technology Innovation Hub at the IIT Ropar in the framework of National Mission on Interdisciplinary Cyber-Physical Systems. We also thank Devendra Raj for his help in data collection.
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Kumari, P., Kumar, A., Hu, MC., Saini, M. (2022). Automatic Double Contact Fault Detection in Outdoor Volleyball Videos. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_11
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