At present, there are efficiency problems in related algorithms for athlete detection and recognition. Based on this, this study analyzes the characteristics of athletes’ sports process. In this study, the Otsu method was used to perform grayscale feature processing. At the same time, based on the Harris corner extraction algorithm, this study proposes that the multi-target tracking combined with the corner feature of the target can be used to track different parts of the athlete as different target areas. In addition, this study uses a sequential algorithm to perform connected component labeling. Finally, in order to test the performance and recognition efficiency of the proposed algorithm, the performance of the algorithm is explored through experimental analysis. The research shows that the algorithm has good performance and has certain practical effects, and it has certain reference significance for subsequent related research.
MingY. and ShiJ., Mesh motion scale invariant feature and collaborative learning for visual recognition, Multimedia Tools & Applications78(12) (2018), 1–18.
2.
WangP., LiW., OgunbonaP., et al., RGB-D-based Human Motion Recognition with Deep Learning: A Survey, Computer Vision and Image UnderstandingPP(1) (2018), 1–22.
3.
WangP., LiuH., WangL., et al., Deep learning-based human motion recognition for predictive context-aware human-robot collaboration, Manufacturing Technology67(1) (2018), 17–20.
4.
GurbuzS.Z. and AminM.G., Radar-Based Human-Motion Recognition With Deep Learning: Promising applications for indoor monitoring, IEEE Signal Processing Magazine36(4) (2019), 16–28.
5.
YuanZ., DuX., NiuH., et al., Motion recognition by a liquid filled tubular triboelectric nanogenerator, Nanoscale 2019, 11.
6.
HuaJ., LiG., JiangD., et al., An Optimized Selection Method of Channel Numbers and Electrode Layouts for Hand Motion Recognition, International Journal of Humanoid Robotics36(3) (2019), 178–184.
7.
LeiL., PengY., ZuojunL., et al., Human Motion Intent Recognition Based on Kernel Principal Component Analysis and Relevance Vector Machine, Robot39(5) (2017), 661–669.
8.
XuanB., LiuZ., ChenL., et al., Motion intention recognition and control of above knee prosthesis, Journal of Southeast University47(6) (2017), 1107–1116.
9.
YanW., XuG., LiM., et al., Steady-State Motion Visual Evoked Potential (SSMVEP) Based on Equal Luminance Colored Enhancement, PLOS ONE12(1) (2017), e0169642.
10.
BrockH. and OhgiY., Assessing Motion Style Errors in Ski Jumping Using Inertial Sensor Devices, IEEE Sensors JournalPP(99) (2017), 1–1.
11.
AvolaD., BernardiM., CinqueL., et al., Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition, IEEE Transactions on Multimedia 2018.
12.
SokolovaA. and KonushinA., Gait Recognition Based on Convolutional Neural Networks, ISPRS–International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesXLII-2/W4 (2017), 207–212.
13.
WuH., WangD., HuangQ., et al., Real-time continuous recognition of knee motion using multi-channel mechanomyography signals detected on clothes, Journal of Electromyography and Kinesiology38 (2017), 94.
14.
CastañoF.A., HernándezA.M., Motion Artifacts Recognition in Electrocardiographic Signals through Artificial Neural Networks and Support Vector Machines for Personalized Health Monitoring, 2017.
15.
HuangT., RuS.R., ZengZ.H., et al., Research on motion recognition algorithm based on bag-of-words model, Microsystem Technologies92(9) (2019), 51–63.
16.
EdmundsT. and CaplierA., Motion-based countermeasure against photo and video spoofing attacks in face recognition, Journal of Visual Communication and Image Representation50 (2017), 314–332.
17.
PaulA., JiangY.C., WangJ.F. and YangJ.F., Parallel reconfigurable computing-based mapping algorithm for motion estimation in advanced video coding, ACM Transactions on Embedded Computing Systems (TECS)11(S2) (2012), 1–18.
18.
RathoreM.M., PaulA., HongW.H., SeoH.C., AwanI. and SaeedS., Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data, Sustainable Cities and Society40 (2018), 600–610.
19.
PaulA., Internet of Things: A primer, R Jeyaraj Human Behavior and Emerging Technologies1(1) (2019), 37–47.
20.
PaulA., DanielA., AhmadA. and RhoS., Cooperative cognitive intelligence for internet of vehicles, IEEE Systems Journal, 11(3) (2017), 1249–1258.
21.
ZengY., Urban and Rural Multi-objective Programming Based on Augmented Lagrange Multiplier Method for Nonlinear Mathematical Equations, Dynamic Systems and Applications29(3) (2020), 783–792.
22.
GangJ., Zhi-QiangY., Yong-RuiT. and Jian-HuaY., Search on Application of GM(1,1) Model Based on Kalman Filtering in Monitoring of Zhengzhou-Xi’an Dedicated Passenger Line, Dynamic Systems and Applications29(3) (2020), 979–988.
23.
PaulA., AhmadA., RathoreM.M. and JabbarS., Smartbuddy: defining human behaviors using big data analytics in social internet of things, IEEE Wireless Communications23(5) (2016), 68–74.
