足球视频球员感知跟踪算法  

Players-aware tracking algorithm in soccer video

作　　者：冯思佳 宋子恺 于俊清[1,2] 何云峰 管涛[1] Feng Sijia;Song Zikai;Yu Junqing;He Yunfeng;Guan Tao(School of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,China;Center of Network and Computation,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学计算机科学与技术学院,武汉430074 [2]华中科技大学网络与计算中心,武汉430074

出　　处：《中国图象图形学报》2021年第7期1668-1680,共13页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61572211)。

摘　　要：目的足球比赛视频中的球员跟踪算法为足球赛事分析提供基础的数据支持。但足球比赛中球员跟踪存在极大的挑战:球员进攻、防守和争夺球权时,目标球员可能产生快速移动、严重遮挡和周围出现若干名干扰球员的情况,目前仍没有一种能够完美解决足球比赛中球员跟踪问题的算法。因此如何解决足球场景中的困难,提升球员跟踪的准确度,成为当前研究的热点问题。方法本文在分析足球比赛视频中球员目标特点的基础上,通过融合干扰项感知的颜色模型和目标感知的深度模型,提出并设计了一种球员感知的跟踪算法。干扰项感知的颜色模型分别提取目标、背景和干扰项的颜色直方图,利用贝叶斯公式得到搜索区域中每个像素点属于目标的似然概率。目标感知的深度模型利用孪生网络计算搜索区域与目标的相似度。针对跟踪漂移问题,使用全局跟踪器和局部跟踪器分别跟踪目标整体和目标上半身,并且在两个跟踪器的跟踪结果出现较大差异的时候分析跟踪器有效性并进行定位修正。结果在公共的足球数据集上将本文算法与10个其他跟踪算法进行对比实验,同时对于文本算法进行了局部跟踪器的消融实验。实验结果表明,球员感知跟踪算法的平均有效重叠率达到了0.5603,在存在同队球员和异队球员干扰的情况下,本文算法比排名第2的算法的有效重叠率分别高出3.7%和6.6%,明显优于其他算法,但是由于引入了干扰项感知的颜色模型、目标感知的深度模型以及局部跟踪器等模块增加了算法的时间复杂度,导致本文算法跟踪速度较慢。结论本文总结了跟踪算法的整体流程并分析了实验结果,认为干扰项感知、目标感知和局部跟踪这3个策略在足球场景中的球员跟踪问题中起到了重要的作用,为未来在足球球员跟踪领域研究的继续深入提供了参考依据。Objective Target object tracking is important in computer vision.Player-tracking algorithms in broadcast soccer videos provide basic data support for the analysis of soccer matches.Several challenges occur in soccer player tracking,including a rapid move of the target player,occlusion,and disturbance of similar players when they attack,defend,and scramble for the ball.However,no perfect tracking algorithm specifically for soccer video is available.The following challenges remain in the player tracking of broadcast soccer videos:1)A small patch of target players in the video frame is not conducive to feature extraction.2)Similar players often interfere with the target player.3)Occlusion of the target player by other players often occurs,requiring the algorithm to distinguish intra-class targets.4)Relocating the target after tracking drift is difficult.Thus,a prevalent topic in current research is how to handle the challenges in the soccer scene and improve the accuracy of player tracking.Method Based on a depth analysis of the characteristics of a soccer player,we propose and design a player-aware tracking algorithm by fusing a distractor-aware color model and the target-aware deep model.In the color model,the color histogram of the target player,background,and distractors are extracted.The color model based on the Bayesian classifier aims to identify the foreground target from the background by color information in the search region.Three primary color components in the RGB color space are divided into 16 color regions by uniform quantization.The color histogram of the corresponding region can be obtained by calculating the number of pixels in each color interval.Distractors are non-target candidate regions whose similarity scores are larger than a certain threshold in the response map.As with the foreground-background color model,the color histogram of the target and distractor is counted,and the likelihood probability that the pixel belongs to the target in the target-distractor item is obtained.In the deep mod

关 键 词：计算机视觉 图像处理 目标跟踪 足球球员跟踪 干扰项感知 目标感知 局部跟踪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]