Football Pitch Condition Analysis Based on k-Means Clustering

• Autores: Ugarte Rojas, Héctor Eduardo, Chullo Llave, Boris
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad de Lima
• Repositorio: Revistas - Universidad de Lima
• Lenguaje: inglés
• OAI Identifier: oai:ojs.pkp.sfu.ca:article/5794
• Enlace del recurso: https://revistas.ulima.edu.pe/index.php/Interfases/article/view/5794
• Nivel de acceso: acceso abierto
• Materia: image analysis; k-means algorithm; dominant colors; clustering; football; análisis de imágenes; algoritmo k-means; colores dominantes; fútbol

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.