วิธีการตรวจจับและจัดหมวดหมู่ผู้เล่นในสนามฟุตบอลจากภาพนิ่งด้วยการเรียนรู้เชิงลึกและการวิเคราะห์ฮิตโตแกรม

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. เท this paper, we proposed A Method Of Detection And Classification For Players On Soccer Field From Single Image By Deep Learning Technique And Histogram Analysis. This method consists of four steps: 1) Preprocessing 2) Image preparation 3) Player detection and 4) Player 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-vectors in database, team is classified finally. To test the performance of methods, three videos of soccer match are used. 457 player images are selected. These player images are used for classification. The Fine-K-Nearest Neighbor or Fine-KNN are used as classifier. Accuracy rate is 90.43%. Sensitivity rate is 76.15%. Specificity rate is 96.03%. For Player Detection method 710 images are used, Accuracy rate is 72.68%. Sensitivity rate is 74.68%. Lastly The overall performance 2277 images are used, Accuracy rate is 90.43%. Sensitivity rate is 76.15%. Specificity rate is 94.01%. Based on the results, the proposed method shows excellent performance.