The traditional sports match analysis mostly adopts the method of manual observation and recording, which is not only time-consuming and laborious but also has the defects of subjectivity and inaccuracy in the judgment results, resulting in the deviation of the match data analysis and statistical results. The purpose of this paper is to study an artificial intelligence system that can automatically analyze and evaluate the effect of both sides in volleyball matches. In this paper, the system is divided into two steps: detection and tracking of moving objects, recognition, and classification of players’ behaviors and movements. About moving target detection and tracking, this paper proposes a moving target fast detection framework based on a mixture of mainstream technologies and a MeanShift target tracking method based on Kalman filtering and adaptive target region size. For behavior and action recognition and classification, this paper proposes a classifier combining BP neural network and support vector machine. Experimental results show that the proposed algorithm and classifier are effective. By analyzing the performance of the proposed classifier, the classification accuracy is 98%.
LoureiroM., HurstM., ValongoB., et al.A comprehensive mapping of high-level men’s volleyball gameplay through social network analysis: Analysing serve, side-out, side-out transition and transition, Montenegrin Journal of Sports ence & Medicine6(2) (2017), 35–41.
2.
CostaG.D.C.T., Maia.M.P., Rodrigues RochaAugusto César, et al.Association between the effect of reception and game procedures in high-level Brazilian volleyball: The case of the women’s “Superliga” champion team, Revista Brasileira de Cineantropometria e Desempenho Humano17(6) (2018), 663.
3.
Asai,Yusuke., Effects of the game process on the perception of “streaks” in volleyball games, Taiikugaku Kenkyu62(1) (2017), 21–32.
4.
ElhosenyMohamed, ShehabAbdulaziz and YuanXiaohui, Optimizing robot path in dynamic environments using genetic algorithm and bezier curve, Journal of Intelligent & Fuzzy Systems33(4) (2017), 2305–2316, IOS-Press.
5.
ConejeroM., EcheverríaC.F., SilvaJ.G., et al.Association between the in-game role of the player, decision-making and performance in volleyball, Revista De Psicologia Del Deporte27(2) (2017), 173–180.
6.
LiuX., LiY. and WangQ., Multi-view hierarchical bidirectional recurrent neural network for depth video sequence-based action recognition, International Journal of Pattern Recognition and Artificial Intelligence32(10) (2018), 1850033.
7.
ZhouShuren, KeMaolin and LuoPeng, Multi-camera transfer GAN for person re-identification, J. Vis. Commun. Image Represent59 (2019), 393–400.
8.
LuL. and WangJ., Image processing and recognition algorithm for target tracking, International Journal on Smart Sensing & Intelligent Systems9(1) (2016), 353–376.
9.
LarrainTomás, BernhardJ S, MeryD, et al.Face recognition using sparse fingerprint classification algorithm, IEEE Transactions on Information Forensics & Security12(7) (2017), 1646–1657.
10.
LiM., WuJ., ZuoL., et al.Aircraft target classification and recognition algorithm based on measured data, Dianzi Yu Xinxi Xuebao/Journal of Electronics & Information Technology40(11) (2018), 2606–2613.
11.
PengY., LiL., LiuS., et al.Virtual samples and sparse representation-based classification algorithm for face recognition, Computer Vision Iet13(2) (2019), 172–177.
12.
ElhosenyM., Multi-object Detection and Tracking (MODT) Machine Learning Model for Real-TimeVideo Surveillance Systems, Circuits, Systems, and Signal Processing, First Online: 20 August 2019.
13.
PrathushaP., JyothiS. and MamathaA.D.M., A hybrid implementation of multiclass recognition algorithm for classification of crabs and lobsters, neural, Parallel & Entific Computations26(1) (2018), 75–95.
14.
RamtekeS., GurjarA. and DeshmukhD., A streamlined OCR system for handwritten MARATHI text document classification and recognition using SVM-ACS algorithm, International Journal of Intelligent Engineering & Systems11(3) (2018), 186–195.
15.
ZhaoYing and YangChunshan, Information fusion robust guaranteed cost Kalman estimators with uncertain noise variances and missing measurements, International Journal of Systems Science50(15) (2019), 2853–2869.
16.
ShuY., FanS. and XiongC., Imbalances sample classification algorithm for industrial product defect recognition based on deep learning, IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association30(7) (2018), 255–258.
17.
LuXiaochun and FeiJuntao, Velocity tracking control of wheeled mobile robots by iterative learning control, International Journal of Advanced Robotic Systems13(3) (2016), 1.
18.
YeomSeokwon and WooYong-Hyun, Person-specific face detection in a scene with optimum composite filtering and colour-shape information, International Journal of Advanced Robotic Systems10(1) (2013), 1.
19.
JittawiriyanukoonC., Proposed algorithm for image classification using regression-based pre-processing and recognition models, International Journal of Electrical & Computer Engineering9(2) (2019), 1021–1027.
20.
YangChunshan, YangZhibo and DengZili, Robust weighted state fusion Kalman estimators for networked systems with mixed uncertainties, Information Fusion45 (2019), 246–265.
21.
LiC., YeeL.Y., MaruyamaH., et al.FPGA-based Volleyball Player Tracker, Acm Sigarch Computer Architecture News44(4) (2017), 80–86.
22.
DuL., HeH., ZhaoL., et al.Noise Robust Radar HRRP target recognition based on scatterer matching algorithm, Sensors Journal IEEE16(6) (2016), 1743–1753.
23.
CostaG.D.C.T., EvangelistaB.F.D.B., MaiaM.P., et al.Volleyball: Analysis of attack performed from the backcourt on men’s Brazilian Volleyball Superleague (Brazilian Championship), Revista Brasilra de Cineantropometria e Desempenho Humano19(1) (2017), 233.
24.
PanShirui, HuRuiqi, FungSai-Fu, LongGuodong, JiangJing and ZhangChengqi, Learning Graph Embedding With Adversarial Training Methods. IEEE Transactions on Cybernetics (2019).
25.
HurstM., LoureiroM., ValongoB., et al.Systemic mapping of high-level women’s volleyball using social network analysis: The case of attack coverage, freeball, and downball, Montenegrin Journal of Sports Science & Medicine6(1) (2017), 57–64.
26.
WuQ., WangJ., YangC., et al.Target recognition by texture segmentation algorithm, Expert Systems with Application46(Mar.) (2016), 394–404.
27.
ZengH., TeoK.L., HeY. and WangW., Sampled-data stabilization of chaotic systems based on a T-S fuzzy model, Information Sciences (2019), 262–272.
28.
ZhangY., YangJ. and HouH., The underwater acoustic target recognition algorithm based on evidence clustering, Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University36(1) (2018), 96–102.
29.
FanX., HuS. and HeJ., Target recognition algorithm for maritime surveillance radars based on clustering and random reference classifier, Moshi Shibie Yu Rengong Zhineng/pattern Recognition & Artificial Intelligence30(11) (2017), 983–994.
30.
HeC.J. and MengX.Y., Harmonic Wave radar seeker target recognition algorithm based on polarization invariant, Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology37(6) (2017), 607–612.
31.
ZhouShuren, LuoPeng, JainDeepak Kumar, LanXiangyuan and ZhangYudong, Double-domain imaging and adaption for person re-identification, IEEE Access7 (2019), 103336–103345.
32.
ZhuR., Application of computer identification and location algorithm in small far infrared target recognition of ship under surge interference, Nephron Clinical Practice24(s3) (2017), 171–181.
33.
ChoJ.H., KangC.H. and ParkC.G., An EO/IR image noise type estimation algorithm for improvement of automatic target recognition, Journal of Institute of Control23(2) (2017), 83–88.
34.
MohantySachi Nandan, Laxmi LydiaE., ElhosenyMohamed, Gethami Al OtaibiMajid M. and ShankarK., Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks, Physical Communication, 2020, In Press.
35.
LeiM., LiY. and GaoJ., Small target recognition processing algorithm in complex background, Boletin Tecnico/technical Bulletin55(5) (2017), 181–190.
36.
ChenY.T., XuW., PiaoY.J., et al.Improved hybrid spill-tree algorithm for fast target matching recognition of satellite images, Guangxue Jingmi Gongcheng/optics & Precision Engineering24(9) (2016), 2310–2317.
37.
DuttaAshit Kumar, ElhosenyMohamed, DahiyaVandna and ShankarK., An efficient hierarchical clustering protocol for multihop Internet of vehicles communication, Transactions on Emerging Telecommunications Technologies, First Online 29 July 2019.
38.
WanS., XiaY., QiL., YangY.H. and AtiquzzamanM., Automated colorization of a grayscale image with seed points propagation, IEEE Transactions on Multimedia, 2020.
