A Method of Soccer-Team Identification by Histogram Feature Vector and Support Vector Machine

Abstract

Statistic of an individual player, tactical analysis in soccer-team, and offside event in a soccer game impacts to match results. An important step to analyze information of the individual player is a soccer team classification. In this paper, we proposed a method for classifying the soccer player-team in a match by histogram features and distance classification technique. This method consists of three steps: 1) player detection 2) player segmentation 3) feature extraction and team classification. Firstly, to segment foreground objects, the soccer ground field is removed by comparing the green color level with a threshold. Morphological technique is used to remove noise in foreground image. Region of objects which is smaller than criterial is also removed. Therefore, remain object is defined as players. Player can be detected. For soccer-player-team classification, histogram in RGB layers are used as feature vectors. Using a distance classification and feature vectors in database, team is classified finally. To test the performance of methods, three videos of soccer match are used. 393 player images are selected. These player images are used for classification. The linear support vector machine are used as classifier. Accuracy rate is 98.47%. Sensitivity rate is 100%. Specificity rate is 96.03%. Based on the results, the proposed method shows excellent performance. © 2023 SPIE.