Football matches not only showcase athletes’ skills and spirit but also foster sports culture and social cohesion. This study designs an improved Faster Region-based Convolutional Neural Network-based target detection model to extract football players’ movement routes. The model enhances feature extraction using residual and feature pyramid networks and optimizes Anchor boxes with binary K-means clustering. A similarity matrix integrating motion and appearance features is then used for movement route extraction. The results show that the accuracy and recall of the detection model are excellent, and the intersection over union ratio and precision mean are also better than the comparison model. Meanwhile, in various scenarios, the accuracy of the mobile route extraction algorithm is at a high level, and the average frame rate performs well. The results demonstrate the effectiveness of the detection model and extraction algorithm in analyzing player movement, providing valuable insights for tactical analysis.
AnzerGBauerP. Determining the difficulty of a pass in football (soccer) using spatio-temporal data. Data Min Knowl Discov2022; 36(1): 295–317. DOI: 10.1007/s10618-021-00810-3.
2.
WangJJiaHBaiX, et al.Research on the location of railway train in tunnel based on factor graph optimization. Appl Comput Lett2023; 7(1).
3.
RaabeDNabbenRMemmertD. Graph representations for the analysis of multi-agent spatiotemporal sports data. Appl Intell2023; 53(4): 3783–3803. DOI: 10.1007/s10489-022-03631-z.
4.
ElugbadeboOJOrunsoluAAAkinyeleSA, et al.. An efficient and secured graphical authentication system. Acta inform Malays2022; 6(1): 17–21. DOI: 10.26480/aim.01.2022.17.21.
5.
ZhangYFuQShanG. A multi-sensor-system cooperative scheduling method for ground area detection and target tracking. Front Inform Technol Electron Eng2023; 24(2): 245–258. DOI: 10.1631/FITEE.2200121.
6.
RoshanGShankerPCBalRG. Study of chronological order in intersecting printed and pen strokes with the help of chromaticity diagram. Acta Scientifica Malaysia2022; 6(2): 38–42.
7.
JiangJXuanSFuJ. Transformer single target tracking algorithm integrating spatio-temporal information. Comput Eng Appl2024; 60(19): 230–241. DOI: 10.3778/j.issn.1002-8331.2307-0069.
8.
LiXLuoRIslamFU. Tracking and detection of basketball movements using multi-feature data fusion and hybrid YOLO-T2LSTM network. Soft Comput2024; 28(2): 1653–1667. DOI: 10.1007/s00500-023-09512-y.
9.
BornZMundtMMianA, et al.The eye in the sky-A method to obtain on-field locations of Australian rules football athletes. AI2024; 5(2): 733–745. DOI: 10.3390/ai5020038.
10.
WangB. Football sports video tracking and detection technology based on YOLOv5 and DeepSORT. Discov Appl Sci2025; 7(6): 1–17. DOI: 10.1007/s42452-025-07116-9.
11.
OngPChongTKOngKM, et al.Tracking of moving athlete from video sequences using flower pollination algorithm. Vis Comput2022; 38(3): 939–962. DOI: 10.1007/s00371-021-02060-2.
12.
ZhangLDaiH. Motion trajectory tracking of athletes with improved depth information-based KCF tracking method. Multimed Tools Appl2023; 82(17): 26481–26493. DOI: 10.1007/s11042-023-14929-6.
13.
ZhongYLiangX. Using CNN-VGG 16 to detect the tennis motion tracking by information entropy and unascertained measurement theory. Adv Nano Res2022; 12(2): 223–239. DOI: 10.12989/anr.2022.12.2.223.
14.
MagloAOrcesiADenizeJ, et al.Individual locating of soccer players from a single moving view. Sensors2023; 23(18): 7938. DOI: 10.3390/s23187938.
15.
WuCZhaoXJinG. Application of intelligent analysis technology of football video based on online target tracking algorithm of motion characteristics in football training. Comput Intell Neurosci2022; 2022(1): 4739712. DOI: 10.1155/2022/4739712.
16.
RaphaelLMeryDY. Mapping fire blight cankers and autumn blooming in pear trees using Faster R-CNN. Precis Agric2024; 25(1): 396–411. DOI: 10.1007/s11119-023-10077-x.
17.
WangZBaiLSongG, et al.Optimized faster R-CNN for oil wells detection from high-resolution remote sensing images. Int J Remote Sens2023; 44(22): 6897–6928. DOI: 10.1080/01431161.2023.2275322.
18.
Mubarak SuudH. An image processing approach for monitoring soil plowing based on drone rgb images. Big data Agr2022; 5(1): 01–05.
19.
ZhaiJSunZHuyanJ, et al.Feature representation improved Faster R-CNN model for high-efficiency pavement crack detection. Can J Civ Eng2022; 50(2): 114–125. DOI: 10.1139/cjce-2022-0137.
20.
AnandRLakshmiSVPandeyDPBK, et al.An enhanced ResNet-50 deep learning model for arrhythmia detection using electrocardiogram biomedical indicators. Evol Syst2024; 15(01): 83–97. DOI: 10.1007/s12530-023-09559-0.
KarthikaSDurgadeviMRaniTY. Enhancing diabetic retinopathy diagnosis with ResNet-50-based transfer learning: a promising approach. Ann Data Sci2024; 11(1): 1–24. DOI: 10.1007/s40745-023-00494-0.
23.
ZarifSMoradEAminK, et al.Video summarization approach based on binary robust invariant scalable keypoints and bisecting K-means. Comput Mater Contin2024; 78(3): 3565–3583. DOI: 10.32604/cmc.2024.046185.
24.
PengLTangQDaiN, et al.Prediction of loom machine status based on binary K-means theory. Fangzhi Xuebao/J Textile Res2023; 44(05): 112–118. DOI: 10.13475/j.fzxb.20220100801.
25.
BudiarsaRWardoyoRMusdholifahA. Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network. Int J Electr Comput Eng2024; 14(03): 3256–3265. DOI: 10.11591/ijece.v14i3.pp3256-3265.
26.
ZhangLLouYSongW, et al.Performance enhancement of PPP/SINS tightly coupled navigation based on improved robust maximum correntropy kalman filtering. Adv Space Res2024; 74(05): 2078–2091. DOI: 10.1016/j.asr.2024.05.072.
27.
ShaoT. Distributed consensus Kalman filtering for asynchronous multi-rate sensor networks. Signal Image Video Process2024; 18(8): 6419–6429. DOI: 10.1007/s11760-024-03326-7.
28.
AkramiAMohsenian-RadH. Event-triggered distribution system state estimation: sparse kalman filtering with reinforced coupling. IEEE Trans Smart Grid2024; 15(01): 627–640. DOI: 10.1109/TSG.2023.3270421.
29.
GuJFuYHungK. On intelligent placement decision-making algorithms for wireless digital twin networks via bandit learning. IEEE Trans Veh Technol2024; 73(06): 8889–8902. DOI: 10.1109/TVT.2024.3360959.
30.
WangCMingYLiuH, et al.Secure and flexible data sharing with dual privacy protection in vehicular digital twin networks. IEEE Trans Intell Transport Syst2024; 25(9): 12407–12420. DOI: 10.1109/TITS.2024.3368342.
31.
GroumposPP. A critical historic overview of artificial intelligence: issues, challenges. Opportunities, and Threats, AIA2023; 1(4): 197–213. DOI: 10.47852/bonviewAIA3202689.
32.
LinCLinWT. A systematic weighted-Hungarian-algorithm for optimization and postoptimal analysis of transportation problem. J Stat Manag Syst2023; 26(04): 843–866. DOI: 10.47974/JSMS-936.
33.
YaoPWuKLouY. Path planning for multiple unmanned surface vehicles using glasius bio-inspired neural network with Hungarian algorithm. IEEE Syst J2018; 17(03): 3906–3917. DOI: 10.1109/JSYST.2022.3222357.
34.
MerzahBMCroockMSRashidAN. Football player tracking and performance analysis using the OpenCV library. Math Model Eng Probl2024; 11(1): 123–132. DOI: 10.18280/mmep.110113.
35.
ChenGLiuS. Constructing a basic training system for football in universities based on object detection and tracking algorithms. Revista Multidisciplinar De Las Ciencias Del Deporte2024; 24(96): 120–145. DOI: 10.15366/rimcafd2024.96.008.
36.
LiuG. Mathematical method to construct the linear programming of football training. Appl Math Nonlinear Sci2023; 8(1): 437–442. DOI: 10.2478/amns.2022.2.00026.
37.
ÖttingMKarlisD. Football tracking data: a copula-based hidden Markov model for classification of tactics in football. Ann Oper Res2023; 325(1): 167–183. DOI: 10.1007/s10479-022-04660-0.
