Soccer Players Detection Using GDLS Optimization and Spatial Bitwise Operation Filter

A. Wibawa, A. N. Rumaksari
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Abstract

Advancement computer vision technology in order to help coach creates strategy has been affecting the sport industry evolving very fast. Players movement patterns and other important behavioral activities regarding the tactics during playing the game are the most important data obtained in applying computer vision in Sport Industry. The basic technique for extracting those information during the game is player detection. Three fundamental challenges of computer vision in detecting objects are random object’s movement, noise and shadow. Background subtraction is an object’s detection method that used widely for separating moving object as foreground and non moving object as background. This paper proposed a method for removing shadow and unwanted noise by improving traditional background subtraction technique. First, we employed GDLS algorithm to optimize background-foreground separation. Then, we did filter shadows and crumbs-like object pixels by applying digital spatial filter which is created from implementation of digital arithmetic algorithm (bitwise operation). Finally, our experimental result demonstrated that our algorithm outperform conventional background subtraction algorithms. The experiments result proposed method has obtained 80.5% of F1-score with average 20 objects were detected out of 24 objects.
基于GDLS优化和空间位运算滤波器的足球运动员检测
计算机视觉技术的进步以帮助教练员制定策略已经影响着体育产业的快速发展。运动员在比赛过程中的动作模式和其他与战术有关的重要行为活动是计算机视觉在体育产业中的应用所获得的最重要的数据。在游戏中提取这些信息的基本技术是玩家检测。计算机视觉在物体检测方面面临的三个基本挑战是随机物体的运动、噪声和阴影。背景减法是一种广泛应用于分离运动目标作为前景和不运动目标作为背景的目标检测方法。本文提出了一种通过改进传统的背景减除技术来去除阴影和有害噪声的方法。首先,采用GDLS算法对背景前景分离进行优化。然后,我们使用数字空间滤波器对阴影和屑状物体像素进行滤波,该滤波器是由数字算术算法(按位运算)实现的。最后,我们的实验结果表明,我们的算法优于传统的背景减法算法。实验结果表明,该方法在24个目标中平均检测出20个目标,达到了f1得分的80.5%。
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