Abstract
This article takes the basketball game video with high attention in sports video as an example to analyze the feature extraction of basketball game video, improve the gray neural network algorithm, and disassemble the basketball video. Moreover, this paper takes basketball, basket, and athletes as the feature extraction objects. Considering that the basketball is a round sphere and the object in the image is a circle, as well as the edge is added to the original image and saved. In addition, this paper combines the improved gray neural network algorithm to construct a basketball motion video target tracking algorithm. Finally, this paper designs experiments to verify the performance of this method. The experimental test results show that this method can effectively recognize basketball gestures with high recognition accuracy, which provides a new method for basketball posture recognition.
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Pai PF, ChangLiao LH, Lin KP (2017) Analyzing basketball games by a support vector machines with decision tree model. Neural Comput Appl 28:4159–4167
Fu X, Zhang K, Wang C et al (2020) Multiple player tracking in basketball court videos[J]. J Real-Time Image Proc 17(6):1811–1828
Kwon J, Kim K, Cho K (2017) Multi-target tracking by enhancing the kernelised correlation filter-based tracker[J]. Electron Lett 53(20):1358–1360
Fallatah MI (2021) Networks, knowledge, and knowledge workers’ mobility: evidence from the National Basketball Association. J Knowl Manag 25(5):1387–1405
Zhao B, Liu S (2021) Basketball shooting technology based on acceleration sensor fusion motion capture technology[J]. EURASIP J Adv Signal Process 2021(1):1–14
Zheng C, Zhang Z, Liu Z et al (2017) Research on video game scene annotation in basketball video[J]. Int J Multim Ubiquitous Eng 12(1):281–290
Liang X, Shen M, Du G et al (2019) Real-time moving target tracking algorithm of UAV/UGV heterogeneous collaborative system in complex background[J]. Univ Politehnica Bucharest Scientif Bull Ser C-Elect Eng Comput Sci 81(1):119–136
Zhang J, Jin X, Sun J et al (2020) Spatial and semantic convolutional features for robust visual object tracking[J]. Multim Tools Appl 79(21):15095–15115
Liu L, Hodgins J (2018) Learning basketball dribbling skills using trajectory optimization and deep reinforcement learning[J]. ACM Trans Gr (TOG) 37(4):1–14
Li T, Sun J, Wang L (2021) An intelligent optimization method of motion management system based on BP neural network[J]. Neural Comput Appl 33(2):707–722
Liu S, Wang S, Liu X et al (2020) Fuzzy detection aided real-time and robust visual tracking under complex environments[J]. IEEE Trans Fuzzy Syst 29(1):90–102
Wang L, Lu H, Yang MH (2017) Constrained superpixel tracking[J]. IEEE Trans Cybern 48(3):1030–1041
Bao H, Lu Y, Wang Q (2020) Single target tracking via correlation filter and context adaptively[J]. Multim Tools Appl 79(37):27465–27482
Bhat PG, Subudhi BN, Veerakumar T et al (2021) Target tracking using a mean-shift occlusion aware particle filter[J]. IEEE Sens J 21(8):10112–10121
Sun K, Li X, Shi W (2018) The fusion of adaptive color attributes for robust compressive tracking[J]. Wireless Pers Commun 102(2):879–894
Feng X, Swaminathan V, Wei S (2019) Viewport prediction for live 360-degree mobile video streaming using user-content hybrid motion tracking[J]. Proc ACM Interactive, Mobile, Wearable Ubiquitous Technol 3(2):1–22
Li K, He F, Yu H et al (2017) A correlative classifiers approach based on particle filter and sample set for tracking occluded target[J]. Appl Math- J Chin Univ 32(3):294–312
Bhat PG, Subudhi BN, Veerakumar T et al (2019) Multi-feature fusion in particle filter framework for visual tracking[J]. IEEE Sens J 20(5):2405–2415
Wang J, Wang Y, Wang K et al (2018) l1-regularized hull representation for visual tracking[J]. J Inf Hiding Multim Signal Process 9(2):313–324
Liu J, Zhong X (2019) An object tracking method based on Mean Shift algorithm with HSV color space and texture features[J]. Clust Comput 22(3):6079–6090
Zafar M, Ali D, Rabeeh Ayaz A (2021) Using machine learning techniques for rising star prediction in basketball. Knowl Based Syst 211:106506
Qi Y, Zhang S, Zhang W et al (2019) Learning attribute-specific representations for visual tracking[C]. Proc AAAI Conf Artif Intell 33(01):8835–8842
Iker Ali O (2020) A novel basketball result prediction model using a concurrent neuro-fuzzy system. Appl Artif Intell 34(13):1038–1054
Ahmad T, Abbas AM (2020) EEAC: an energy efficient adaptive cluster based target tracking in wireless sensor networks[J]. J Interdiscipl Math 23(2):379–392
Wu W, Xu M, Liang Q et al (2020) Multi-camera 3D ball tracking framework for sports video[J]. IET Image Proc 14(15):3751–3761
Cao S, Wang X, Xiang K (2017) Visual object tracking based on motion-adaptive particle filter under complex dynamics[J]. EURASIP J Image Video Process 2017(1):1–21
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Wang, T., Shi, C. Basketball motion video target tracking algorithm based on improved gray neural network. Neural Comput & Applic 35, 4267–4282 (2023). https://doi.org/10.1007/s00521-022-07026-6
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DOI: https://doi.org/10.1007/s00521-022-07026-6