Abstract
Performance analysis is essential in sports practice where the athlete is evaluated to improve their performance. Due to the rapid growth of science and technology, research on automated recognition of sports actions has become ubiquitous. The implementation of automated action recognition is an effort to overcome the manual action recognition in sport performance analysis. In this study, we developed a model for automated badminton action recognition from the computer vision data inputs using the deep learning pre-trained AlexNet Convolutional Neural Network (CNN) for features extraction and classify the features using supervised machine learning method which is linear Support-Vector Machine (SVM). The data inputs consist of badminton match images of two classes: hit and non-hit action. Before pre-trained AlexNet CNN was directly extracting the features, we introduced the new local CNN extractor in recognition pipeline. The results show that the classification accuracy with this new local CNN method achieved 98.7%. In conclusion, this new local CNN extractor can contribute to the improvement of the performance accuracy of the classification task.
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References
Cust, E.E., et al.: Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance. J. Sports Sci. 37(5), 568–600 (2019)
Zerrouki, N., et al.: Vision-based human action classification using adaptive boosting algorithm. IEEE Sens. J. 18(12), 5115–5121 (2018)
Tejero-de-Pablos, A., et al.: Human action recognition-based video summarization for RGB-D personal sports video. In: 2016 IEEE International Conference on Multimedia and Expo (ICME) (2016)
Han, Y.M., et al.: Going deeper with two-stream ConvNets for action recognition in video surveillance. Pattern Recogn. Lett. 107, 83–90 (2018)
Karpathy, A., et al.: Large-scale video classification with convolutional neural networks, pp. 1725–1732 (2014)
Sozykin, K., et al.: Multi-label class-imbalanced action recognition in hockey videos via 3D convolutional neural networks. Computer Vision and Pattern Recognition, abs/1709.01421 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition CoRR, abs/1409.1556 (2014)
Qin, J., et al.: Compressive sequential learning for action similarity labeling. IEEE Trans. Image Process. 25(2), 756–769 (2016)
Baek, J., Yun, B.-J.: Posture monitoring system for context awareness. Mob. Comput. 59, 1589–1599 (2010)
San-Segundo, R., et al.: Human activity monitoring based on hidden Markov models using a smartphone. IEEE Instrum. Meas. Mag. 19(6), 27–31 (2016)
Tao, Y., Hu, H.: A novel sensing and data fusion system for 3-D arm motion tracking in telerehabilitation. IEEE Trans. Instrum. Meas. 57, 1029–1040 (2008)
Yu, L., et al.: Nonintrusive appliance load monitoring for smart homes: recent advances and future issues. IEEE Instrum. Meas. Mag. 19(3), 56–62 (2016)
Mlakar, M., et al.: Analyzing tennis game through sensor data with machine learning and multi-objective optimization. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, pp. 153–156. ACM, Maui (2017)
Renò, V., et al.: A technology platform for automatic high-level tennis game analysis. Comput. Vis. Image Underst. 159, 164–175 (2017)
Cheng, J., et al.: Designing sensitive wearable capacitive sensors for activity recognition. IEEE Sens. J. 13(10), 3935–3947 (2013)
Gaddam, A., Mukhopadhyay, S.C., Gupta, G.S.: Elder care based on cognitive sensor network. IEEE Sens. J. 11(3), 574–581 (2011)
Kan, Y., Chen, C.: A wearable inertial sensor node for body motion analysis. IEEE Sens. J. 12(3), 651–657 (2012)
Wang, J., et al.: Learning actionlet ensemble for 3D human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 914–927 (2014)
Saif, S., Tehseen, S., Kausar, S.: A survey of the techniques for the identification and classification of human actions from visual data. Sensors 18(11), 3979 (2018)
Yoshikawa, F., et al.: Automated service scene detection for badminton game analysis using CHLAC and MRA. World Acad. Sci. Eng. Technol. 4, 841–844 (2010)
Zhao, Y., et al.: Recognizing human actions from low-resolution videos by region-based mixture models. In: 2016 IEEE International Conference on Multimedia and Expo (ICME) (2016)
Shishido, H., et al.: A trajectory estimation method for badminton shuttlecock utilizing motion blur. In: Image and Video Technology, pp. 325–336. Springer, Heidelberg (2013)
Ma, Z., Sun, Z.: Time-varying LSTM networks for action recognition (2018)
Yang, J.B., et al.: Deep convolutional neural networks on multichannel time series for human activity recognition. In: IJCAI, Buenos Aires, Argentina (2015)
Alex, K., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks, pp. 1097–1105 (2012)
Shih, H.: A survey on content-aware video analysis for sports. IEEE Trans. Circuits Syst. Video Technol. 28(5), 1212–1231 (2017)
Tora, M.R., Chen, J., Little, J.J.: Classification of puck possession events in ice hockey. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017)
Baccouche, M., et al.: Action classification in soccer videos with long short-term memory recurrent neural networks. In: Artificial Neural Networks – ICANN 2010: 20th International Conference. Springer, Heidelberg (2010)
Ullah, A., et al.: Action recognition in video sequences using deep bi-directional LSTM with CNN features. IEEE Access 6, 1155–1166 (2018)
Acknowledgement
The authors would like to express their gratitude to Universiti Teknologi Malaysia (UTM) and the Minister of Education (MOE), Malaysia for supporting this research work under Zamalah UTM and FRGS Research Grant No. R.J130000.7851.5F108.
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Rahmad, N.A., As’ari, M.A., Ibrahim, M.F., Sufri, N.A.J., Rangasamy, K. (2020). Vision Based Automated Badminton Action Recognition Using the New Local Convolutional Neural Network Extractor. In: Hassan, M., et al. Enhancing Health and Sports Performance by Design. MoHE 2019. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-3270-2_30
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DOI: https://doi.org/10.1007/978-981-15-3270-2_30
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