@inproceedings{c1207b895d614065bff402e1f1b4cad1,
title = "CASA: Cricket Action Similarity Assessment in Video Footage Using Deep Metric Learning",
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%.",
keywords = "Cricket action similarity, Inception Resnet V2, Siamese network, Xception",
author = "Tevin Moodley and {van der Haar}, Dustin",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 3rd Southern African Conference on Artificial Intelligence Research, SACAIR 2022 ; Conference date: 05-12-2022 Through 09-12-2022",
year = "2022",
doi = "10.1007/978-3-031-22321-1_10",
language = "English",
isbn = "9783031223204",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "139--153",
editor = "Anban Pillay and Edgar Jembere and Aurona Gerber",
booktitle = "Artificial Intelligence Research - Third Southern African Conference, SACAIR 2022, Proceedings",
address = "Germany",
}