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Automatic Soccer Players Tracking In Goal Scenes By Camera Motion Elimination

机译:自动足球运动员通过消除摄像机运动来跟踪目标场景

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摘要

In this paper, we propose a novel and effective algorithm for tracking soccer players in goal scenes, by eliminating fast camera motions effect through the correspondence between line marks in soccer field model and image sequences. The proposed algorithm comprises four steps. At the first step, we introduce an automatic grass field extraction algorithm that is tested for various soccer video types and conditions. The field line marks, which are used to locate the players, are detected at the second step using Hough Transform and tracked in all frames by predicting their positions using Kalman Filter. At the third step, we introduce a novel approach for estimating the players' positions during the course of tracking players. We estimate a player's position in the current frame by observing his position in the old frame within the soccer field model, using Perspective Transformation of the old frame to the real world coordinate system, and then by projecting back the obtained player's position into the current frame. At the final step, the players are tracked by applying the region-based detection algorithm, the Histogram Back-Projection algorithm or a combination of the Merge-Split approach and the Template-Matching algorithm around the estimated positions, depending on whether or not the occlusion has occurred and if yes, how it has occurred. Then their memberships are identified by an algorithm that employs both appearance and spatial information of the players. Image sequences of different soccer games were captured from different sources, and all experiments were performed off-line. The results of our experimentations show that our algorithm is highly robust to occlusion, different soccer field colours, different lights such as sunlight and spotlight, shadows of players and fading the whole screen due to fast camera movements.
机译:在本文中,我们通过消除足球场模型中线条标记与图像序列之间的对应关系来消除快速摄像机运动的影响,提出了一种新颖有效的跟踪目标场景中足球运动员的算法。所提出的算法包括四个步骤。第一步,我们介绍一种自动草地提取算法,该算法已针对各种足球视频类型和条件进行了测试。第二步使用霍夫变换检测用于定位球员的场线标记,并通过使用卡尔曼滤波器预测其位置在所有帧中进行跟踪。在第三步中,我们引入了一种新颖的方法来估计玩家跟踪过程中的位置。我们通过观察球员在足球场模型中旧框架中的位置,使用旧框架的透视变换到现实世界坐标系,然后将获得的球员位置投影回当前框架中,来估计他在当前框架中的位置。在最后一步,根据估计位置附近的位置,应用基于区域的检测算法,直方图反投影算法或Merge-Split方法和Template-Matching算法的组合来跟踪玩家。发生了咬合,如果是,则如何发生。然后,通过使用玩家的外观和空间信息的算法来识别其成员资格。从不同的来源捕获了不同足球比赛的图像序列,并且所有实验都是离线进行的。实验结果表明,我们的算法对于遮挡,足球场不同的颜色,阳光和聚光灯等不同的光,球员的阴影以及由于快速摄像机移动而导致的整个屏幕褪色具有很高的鲁棒性。

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