TY - GEN
T1 - CASRM
T2 - 11th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
AU - Moodley, Tevin
AU - van der Haar, Dustin
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - With the rapid changes within sport, specifically cricket, technology has been used to cater to the challenges faced within the domain. However, research within the field of study has shown that there is a gap to bridge in the way of establishing a cost-effective means to recognize different cricketing strokes. In our previous work, feature extraction methods such as Histogram of orientated gradients with support vector machines, K-nearest neighbor, and the AlexNet architecture were used to achieve cricket stroke recognition. While promising results were obtained, this article will attempt to exploit OpenPose skeleton keypoints, which will be used as a set of descriptive features that will be fed into the Long Short-Time Memory architecture for cricket stroke recognition. By applying the OpenPose skeleton to the dataset, the model can capture the pose keypoints of the cricket batsmen, whereby the body part locations and detection confidence are presented as a feature vector. The image dataset, which was compiled in a previous study, is used to ensure a fair measure of the proposed model. The strokes that will be addressed are as follows: block, cut, drive and glance. The Long Short-Time Memory architecture outperformed previously tested classifiers with a recorded model accuracy of 81.25%. The results suggest the model is capable of recognizing different cricket strokes. As a result, a human-computer interaction system can be developed to assist coaches and spectators to gain further understanding within the domain.
AB - With the rapid changes within sport, specifically cricket, technology has been used to cater to the challenges faced within the domain. However, research within the field of study has shown that there is a gap to bridge in the way of establishing a cost-effective means to recognize different cricketing strokes. In our previous work, feature extraction methods such as Histogram of orientated gradients with support vector machines, K-nearest neighbor, and the AlexNet architecture were used to achieve cricket stroke recognition. While promising results were obtained, this article will attempt to exploit OpenPose skeleton keypoints, which will be used as a set of descriptive features that will be fed into the Long Short-Time Memory architecture for cricket stroke recognition. By applying the OpenPose skeleton to the dataset, the model can capture the pose keypoints of the cricket batsmen, whereby the body part locations and detection confidence are presented as a feature vector. The image dataset, which was compiled in a previous study, is used to ensure a fair measure of the proposed model. The strokes that will be addressed are as follows: block, cut, drive and glance. The Long Short-Time Memory architecture outperformed previously tested classifiers with a recorded model accuracy of 81.25%. The results suggest the model is capable of recognizing different cricket strokes. As a result, a human-computer interaction system can be developed to assist coaches and spectators to gain further understanding within the domain.
KW - Confusion matrix
KW - Cricket stroke recognition
KW - LSTM
KW - ROC
UR - http://www.scopus.com/inward/record.url?scp=85088743022&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-49904-4_5
DO - 10.1007/978-3-030-49904-4_5
M3 - Conference contribution
AN - SCOPUS:85088743022
SN - 9783030499037
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 67
EP - 78
BT - Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Posture, Motion and Health - 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
A2 - Duffy, Vincent G.
PB - Springer
Y2 - 19 July 2020 through 24 July 2020
ER -
