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摘要:
足球比赛中球员运动数据分析对增加观众的观看体验和辅助教练进行球员评估有着重要意义。球员运动数据分析的难点在于如何定位球员在球场上的坐标，即如何确定足球视频中单帧画面出现的缺损球场与标准二维球场之间的映射关系。针对如何在足球比赛中克服相机的高速移动和视角剧烈变化，设计并提出了利用球场重建与球员跟踪来进行球员运动数据分析的方法。球场重建方面，将足球视频中的球场分组为左中右3部分，每组通过球场分割、球场直线检测、球场直线分组、球场中圈点集合识别和球场关键点匹配来实现缺损球场到标准球场的映射；球员跟踪采用核相关滤波(KCF)跟踪算法，得到了球员运动数据统计的可视化结果。结合球场重建和球员跟踪算法定位球员的标准坐标，统计球员的一系列运动数据并进行可视化分析。提出的球员运动数据分析方法能够准确而快速地统计出球员的运动数据，包括球员坐标、运动轨迹、奔跑速度、活动范围和球员间距。球场重建方面采用图像交并进行评估，交并比达到87%，相比于传统的基于字典查询的方法(交并比为83.3%)准确度提升了3.7%。实验结果表明：所提出的球场重建方法能够更准确地表示球场映射关系，为球员运动数据分析统计提供更好的支持。
Abstract:Objective In soccer matches, player data analysis is crucial to improve the viewing experience for viewers and to aid coaches in performance evaluation. The difficulty of player data analysis is how to locate the coordinates of players on the soccer field, i.e., how to determine the mapping relationship between the defective field appearing in a frame of soccer video and the standard two-dimensional field. Aiming at how to deal with the high-speed movement of the camera and the sharp change of the angle of view in the soccer match, we designed and proposed a method of player motion analysis using field reconstruction and player tracking. For field reconstruction, the field in the soccer video is grouped into three parts: left, center, and right. Each group is mapped from the defective field to the standard field by soccer field segmentation, straight line detection, straight-line grouping, center circle point set identification, and key point matching; the kernelized correlation filter (KCF) tracking algorithm is used for player tracking. Then, using a combination of field reconstruction and player tracking approaches, we determine the standard coordinates of players and generate a set of player motion data and visualization results.The player data analysis method proposed in this paper can accurately and effectively count the player data, including player coordinates, motion trajectory, running speed, activity range, and player spacing. In terms of field reconstruction, image intersection is used for evaluation, and the intersection ratio of our algorithm reaches 87%, which improves 3.7% compared to the traditional dictionary-based reconstruction method (83.3% intersection ratio). The results of the experiments suggest that our field reconstruction method can more precisely depict the field mapping connection and can give greater assistance for the statistical analysis of player data.In this paper, we design and propose a complete algorithm for player data analysis based on soccer field reconstruction and obtain visualization results of player statistics. The soccer field reconstruction method combining the knowledge of soccer has improved in accuracy and efficiency. The player data analysis in this paper can provide data support for soccer fans and practitioners, the field reconstruction method lays a solid foundation for further research in the field of player analysis.
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图 2 半场区域与中场区域的球场分割结果
Figure 2. Segmentation results of half field area and midfield area of soccer field
表 1 速度离散化说明
Table 1. Description of velocity discretization
运动类型分类 速度间隔 站立 小于0.2 m/s 走 0.2~2.1 m/s 慢跑 2.1~3.8 m/s 快跑 3.8~6.1 m/s 冲刺 大于6.1 m/s 
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[1] BALLARD D H. Generalizing the Hough transform to detect arbitrary shapes[J]. Pattern Recognition, 1981, 13(2): 111-122. doi: 10.1016/0031-3203(81)90009-1 [2] HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596. doi: 10.1109/TPAMI.2014.2345390 [3] SHARMA R A, BHAT B, GANDHI V. Automated top view registration of broadcast football videos[C]//Proceedings of IEEE Winter Conference on Applications of Computer Vision. Piscataway: IEEE Press, 2018: 305-313. [4] MIRZA M, OSINDERO S. Conditional generative adversarial nets[EB/OL]. (2014-11-06)[2022-03-01]. https://arxiv.org/abs/1411.1784. [5] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2005, 1: 886-893. [6] CHEN J, LITTLE J J. Sports camera calibration via synthetic data[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE Press, 2019: 2497-2504. [7] LUCAS B. An iterative image registration technique with an application to stereo vision (DARPA)[C]//Proceedings of DARPA Image Understanding Workshop, 1981: 121-130. [8] HOMAYOUNFAR N, FIDLER S, URTASUN R. Sports field localization via deep structured models[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 4012-4020. [9] DETONE D, MALISIEWICZ T, RABINOVICH A. Deep image homography estimation[EB/OL]. (2016-01-13)[2022-03-01]. https://arxiv.org/abs/1606.03798. [10] NGUYEN T, CHEN S W, SHIVAKUMAR S S, et al. Unsupervised deep homography: A fast and robust homography estimation model[J]. IEEE Robotics and Automation Letters, 2018, 3(3): 2346-2353. [11] JIANG W, HIGUERA J, ANGLES B, et al. Optimizing through learned errors for accurate sports field registration[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Piscataway: IEEE Press, 2020: 201-210. [12] CANNY J. Collision detection for moving polyhedra[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(2): 200-209. [13] FENG N, SONG Z, YU J, et al. SSET: A dataset for shot segmentation, event detection, player tracking in soccer videos[J]. Multimedia Tools and Applications, 2020, 79(39): 28971-28992. [14] PUWEIN J, ZIEGLER R, VOGEL J, et al. Robust multi-view camera calibration for wide-baseline camera networks[C]//2011 IEEE Workshop on Applications of Computer Vision (WACV). Piscataway: IEEE Press, 2011: 321-328. [15] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2010: 2544-2550. -
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