Football Pitch Condition Analysis Based on k-Means Clustering
DOI:
https://doi.org/10.26439/interfases2022.n015.5794Palavras-chave:
image analysis, k-means algorithm, dominant colors, clustering, footballResumo
Football, a highly popular sport all over the world, requires that professional footballers practice it on a field of play in ideal conditions, which, among other things, includes the usage and maintenance of healthy natural grass. In this study, we present an unsupervised allocator strategy for image analysis of football pitches that uses k-means clustering and color comparison to assess whether a playing field is in good or bad condition. Our approach considers proportions of dominant RGB colors for automatized decision-making. We developed a prototype and tested it with a series of images; this paper offers a comparison between the findings of this test and our expected results.
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Carter, W. (2020). Corner flag in the soccer field at Brastad arena [Photograph]. Wikimedia Commons. https://commons.wikimedia.org/wiki/File:Soccer_field_at_Brastad_arena_6.jpg
El País. (2018, November 9). Así fue como pintaron el césped del Centenario de verde. https://www.elpais.com.uy/ovacion/futbol/asi-pintaron-cesped-centenario-verde.html
Football NSW Limited. (2015, November 6). Field marking & equipment. A guide to preparing your field for football. https://footballnsw.com.au/wp-content/uploads/2017/06/Field-Markings-and-Equipment.pdf
Forgy, E. W. (1965). Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics, 21, 768-780.
Kassambara, A. (n.d.). Cluster validation statistics: Must know methods. Datanovia. https://www.datanovia.com/en/lessons/cluster-validation-statistics-must-know-methods/
Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. https://doi.org/10.1109/TIT.1982.1056489
Loesdau, M., Chabrier, S., & Gabillon, A. (2014). Hue and Saturation in the RGB Color Space. In A. Elmoataz, O. Lezoray, F. Nouboud, & D. Mammass, (Eds), Image and Signal Processing. ICISP 2014. Lecture Notes in Computer Science, vol 8509. Springer. https://doi.org/10.1007/978-3-319-07998-1_23
Maklaan. (2015). A RGB color cube explained with three diagrams [Diagram]. Wikimedia Commons. https://commons.wikimedia.org/wiki/File:RGB_color_cube.svg
Na, S., Xumin, L., & Yong, G. (2010). Research on k-means clustering algorithm: An improved k-means clustering algorithm. In F. Yu, X. Peng, H. Liu, J. Shiu, & R. Ng (Eds.), Proceedings of the Third International Symposium on Intelligent Information Technology and Security Informatics (pp. 63-67). IEEE Computer Society; Conference Publishing Services. https://doi.org/10.1109/IITSI.2010.74
Pavan Kumar, I., Hara Gopal, V. P., Ramasubbareddy, S., Nalluri, S., & Govinda, K. (2020). Dominant color palette extraction by k-means clustering algorithm and reconstruction of image. In K. Raju, R. Senkerik, S. Lanka, & V. Rajagopal (Eds.), Data engineering and communication technology, vol 1079 (pp. 921–929). Springer Singapore. https://doi.org/10.1007/978-981-15-1097-7_78
Pham, D. T., Dimov, S. S., & Nguyen, C. D. (2005). Selection of K in K-means clustering. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 219(1), 103-119. https://doi.org/10.1243/095440605X8298
Radovanović, B. (2011). NK Zelengaj football pitch in Dugave neighborhood, Zagreb, Croatia [Photography]. Wikimedia Commons. https://commons.wikimedia.org/wiki/File:NK_Zelengaj_football_pitch_20110918_3186.jpg
Rhyne, T.-M. (2016). applying color theory to digital media and visualization. CRC Press. https://doi.org/10.1145/2776880.2792696
Sharma, A. (2021, December 9). How to find the most dominant colors in an image using kmeans clustering —with source code— interesting project. Towards Dev. https://towardsdev.com/how-to-find-the-most-dominant-colors-in-an-image-in-python-using-kmeans-clustering-with-source-527ef3e6775f
Solomon, C. & Breckon, T. (2011). Fundamentals of digital image processing: A practical approach with examples in Matlab. John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470689776
Stockman, G., & Shapiro, L. G. (2001). Computer vision. Pearson.
Szymanski, S. (2014). It’s football not soccer. http://ns.umich.edu/Releases/2014/June14/Its-football-not-soccer.pdf
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Última actualización: 03/05/21
