Abstract
Cricket batters will often measure their performance through comparisons against successful batters or feedback provided by experts. Action Similarity Assessment is the task of comparing the similarity or dissimilarity of an action between two actors to determine how similar the actions they perform are to one another. This research paper proposes the use of a Siamese Convolution Neural Network to compute the similarity distances between different batters using video footage. Due to the limited research surrounding action similarity, a new dataset is proposed to help foster future works pertaining to action similarity. Three architectures are proposed to determine which architecture is best suited for the domain: a custom CNN, Inception Resnet V2, and Xception. From the results obtained, it can be concluded that the best solution for the action similarity assessment task within cricket video footage is a Siamese Xception architecture, achieving a model accuracy of 98%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Appalaraju, S., Chaoji, V.: Image similarity using deep CNN and curriculum learning. arXiv preprint arXiv:1709.08761 (2017)
Articles, C.T.: Cricket basics explaination. http://www.chicagotribune.com/chi-cricket-basics-explanation-gfx-20150215-htmlstory.html
Auerbach, Michael P.M.: Cricket (sport). Salem Press Encyclopedia (2022)
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a “siamese” time delay neural network. Adv. Neural Inf. Process. Syst. 6 (1993)
Chang, J., Sha, J.: An efficient implementation of 2D convolution in CNN. IEICE Electron. Express 14, 20161134 (2016)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Dokmanic, I., Parhizkar, R., Ranieri, J., Vetterli, M.: Euclidean distance matrices: essential theory, algorithms, and applications. IEEE Signal Process. Mag. 32(6), 12–30 (2015)
ESPNcricinfo: abdevilliers (n.d.). https://www.espncricinfo.com/player/ab-de-villiers-44936. Accessed 04 July 2022
Goel, D.: Ab Devilliers 126 Vs Aus in Port Elizabeth (2021). https://www.youtube.com/watch?v=CCRS-z31g60 &t=32s. Accessed 23 June 2022
Jain, H., Harit, G., Sharma, A.: Action quality assessment using Siamese network-based deep metric learning. IEEE Trans. Circ. Syst. Video Technol. 31(6), 2260–2273 (2020)
Kaya, M., Bilge, H.Ş: Deep metric learning: a survey. Symmetry 11(9), 1066 (2019)
Kliper-Gross, O., Hassner, T., Wolf, L.: The action similarity labeling challenge. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 615–621 (2011)
Malkauthekar, M.: Analysis of euclidean distance and manhattan distance measure in face recognition. In: Third International Conference on Computational Intelligence and Information Technology (CIIT 2013), pp. 503–507. IET (2013)
Mitra, S., Acharya, T.: Gesture recognition: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(3), 311–324 (2007)
Moodley, T., van der Haar, D.: Cricket stroke recognition using computer vision methods. In: Kim, K.J., Kim, H.-Y. (eds.) Information Science and Applications. LNEE, vol. 621, pp. 171–181. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-1465-4_18
Nair, V., et al.: Action similarity judgment based on kinematic primitives. In: 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 1–8. IEEE (2020)
Roy, S.K., Harandi, M., Nock, R., Hartley, R.: Siamese networks: the tale of two manifolds. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3046–3055 (2019)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Thirty-first AAAI Conference on Artificial Intelligence (2017)
Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2495–2504 (2021)
Zhou, J., et al.: Skeleton-based human keypoints detection and action similarity assessment for fitness assistance. In: 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP), pp. 304–310. IEEE (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Moodley, T., van der Haar, D. (2022). CASA: Cricket Action Similarity Assessment in Video Footage Using Deep Metric Learning. In: Pillay, A., Jembere, E., Gerber, A. (eds) Artificial Intelligence Research. SACAIR 2022. Communications in Computer and Information Science, vol 1734. Springer, Cham. https://doi.org/10.1007/978-3-031-22321-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-22321-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22320-4
Online ISBN: 978-3-031-22321-1
eBook Packages: Computer ScienceComputer Science (R0)