ORSNet: A Hybrid Neural Network for Official Sports Referee Signal Recognition



Tse-Yu Pan


Chen-Yuan Chang


Wan-Lun Tsai

In this work, we propose a novel sports referee training system based on wearable sensors and a real-time Official Referee Signal (ORS) segmentation/recognition method which can recognize 65 kinds basketball ORSs with the accuracy of 95.3%. A hybrid neural network named ORSNet is designed for recognizing gestures based on IMU signals. The proposed ORSNet involves convolution layers and recurrent layers to learn more representative features and correlations in temporal domain, respectively. A novel loss function and a weight sharing strategy are proposed to learn a more robust ORS recognition model. Moreover, we investigate the influence of applying a semi-supervised network in the proposed ORSNet.



@inproceedings{pan2018orsnet, title = {ORSNet: A Hybrid Neural Network for Official Sports Referee Signal Recognition}, author = {Pan, Tse-Yu and Chang, Chen-Yuan and Tsai, Wan-Lun and Hu, Min-Chun}, booktitle = {Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports (MMSports)}, series = {MMSports'18}, pages = {51--58}, year = {2018}, organization={ACM} }

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