Improving Tracking Soccer Players in Shaded Playfield Video

Abstract

Soccer is the most popular sport in the world and the information extracted from this match has many uses. It can be used to extract players ‘paths, recognize the performance, and evaluate the players‘ statistics, evaluate the referee‘s decision, and so on. One of the main steps in analyzing soccer video is tracking players that seeks to locate players when playing video. Player tracking involves various processes, such as playfield detection, player detection, tracking of players, apparent modeling of players, and identification of players overlapping. One of the challenges in this field is the tracing of players in the shaded play field, which challenges the tracking of players due to light changes in the field. In this paper, using the proposed algorithm to identify and Tracking players in television shows with two shaded playfield and sun shades. The proposed method is identified the playfield by using the saliency map algorithm and shadow elimination, which will minimize the noise from the stadium area. Then by using the features of the color, brightness and edge we will recognize the players. Using the combination of Top-hat transformation and the morphological operation the lines of the playfield is detected. Finally, by using the results of the detection step we will track players. The tracker used in this study is an improved particle filter that uses a combination of color and edge features. The results of the proposed method demonstrate that the detection of play field has 93% accuracy. Also the proposed method tracks the detected players with 90% precision. Therefore tracking accuracy shows that light variations have very little effect on it.