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Goaling recognition based on intelligent analysis of real-time basketball image of Internet of Things

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Abstract

This research aims to study the goaling recognition methods based on the intelligent analysis of real-time basketball images of the Internet of Things, so as to accelerate the development of basketball and other sports fields. First, the status of basketball-related image recognition and tracking in the field of sports is analysed, which are combined based on the background differencing and inter-frame differencing. Then, an improved image intelligent detection is proposed to identify the basketball goaling, and further analysis of the tracking algorithm related to the moving target is conducted. Finally, an improved Camshift target tracking algorithm is proposed to detect and recognize the basketball goaling. The results show that both background differencing and inter-frame differencing have large errors in basketball goaling recognition. The improved image intelligent detection algorithm in this research can fit the contour of the basketball very well, and the fitting effect of the smallest circle is ideal. From the perspective of basketball goaling trajectory tracking, it is found that the improved Camshift goal tracking algorithm has the best recognition effect on basketball goaling. In addition, the average running time of the algorithm is 209 ms, with favourable accuracy. Therefore, verified by the tests, the intelligent detection algorithm and trajectory tracking algorithm proposed in this research have high recognition accuracy for basketball goaling, and the operation efficiency is ideal, which provides experimental reference for the later intelligent development of sports fields such as basketball.

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References

  1. Jing H, Xiaoqiong X (2020) Sports image detection based on FPGA hardware system and particle swarm algorithm [J]. Microprocess Microsyst. https://doi.org/10.1016/j.micpro.2020.103348

    Article  Google Scholar 

  2. Ting DSW, Pasquale LR, Peng L et al (2019) Artificial intelligence and deep learning in ophthalmology [J]. Br J Ophthalmol 103(2):167–175

    Article  Google Scholar 

  3. Nadeem A, Jalal A, Kim K (2020) Accurate physical activity recognition using multidimensional features and markov model for smart health fitness [J]. Symmetry 12(11):1766

    Article  Google Scholar 

  4. Zhong B, Li H, Luo H et al (2020) Ontology-based semantic modeling of knowledge in construction: classification and identification of hazards implied in images [J]. J Constr Eng Manag 146(4):04020013

    Article  Google Scholar 

  5. Lu J, Ma C, Li L et al (2018) A vehicle detection method for aerial image based on YOLO [J]. J Comput Commun 6(11):98–107

    Article  Google Scholar 

  6. Murray NM, Unberath M, Hager GD et al (2020) Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review [J]. J Neurointervent Surg 12(2):156–164

    Article  Google Scholar 

  7. Khan S, Muhammad K, Mumtaz S et al (2019) Energy-efficient deep CNN for smoke detection in foggy IoT environment [J]. IEEE Internet Things J 6(6):9237–9245

    Article  Google Scholar 

  8. van der Kruk E, Reijne MM (2018) Accuracy of human motion capture systems for sport applications; state-of-the-art review [J]. Eur J Sport Sci 18(6):806–819

    Article  Google Scholar 

  9. Cust EE, Sweeting AJ, Ball K et al (2019) Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance [J]. J Sports Sci 37(5):568–600

    Article  Google Scholar 

  10. Li G, Zhang C (2019) Automatic detection technology of sports athletes based on image recognition technology [J]. EURASIP J Image Video Process 2019(1):15

    Article  Google Scholar 

  11. Baker LB, Model JB, Barnes KA et al (2020) Skin-interfaced microfluidic system with personalized sweating rate and sweat chloride analytics for sports science applications [J]. Sci Adv. https://doi.org/10.1126/sciadv.abe3929

    Article  Google Scholar 

  12. Chen WJ, Jhou MJ, Lee TS et al (2021) Hybrid basketball game outcome prediction model by integrating data mining methods for the national basketball association[J]. Entropy 23(4):477

    Article  Google Scholar 

  13. Liu A, Xie H, Ahmed K (2021) Fault detection technology of national traditional sports equipment based on optical microscope imaging technology [J]. Alex Eng J 60(2):2697–2705

    Article  Google Scholar 

  14. Bin L, Xiaoyun C (2020) Home intelligent sports action automation system based on bluetooth [J]. Microprocess Microsyst. https://doi.org/10.1016/j.micpro.2020.103335

    Article  Google Scholar 

  15. Yuan B, Li S, Li N (2018) Multiscale deep features learning for land-use scene recognition [J]. J Appl Remote Sens 12(1):015010

    Article  Google Scholar 

  16. Wei H, Jafari R, Kehtarnavaz N (2019) Fusion of video and inertial sensing for deep learning–based human action recognition [J]. Sensors 19(17):3680

    Article  Google Scholar 

  17. Qin L, Kang L (2018) Application of video scene semantic recognition technology in smart video [J]. Tehnički vjesnik 25(5):1429–1436

    Google Scholar 

  18. Olvera-Barrios A, Heeren TFC, Balaskas K et al (2021) Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images [J]. Br J Ophthalmol 105(2):265–270

    Article  Google Scholar 

  19. Tian Z, Zhan R, Wang W et al (2020) Object detection in optical remote sensing images by integrating object-to-object relationships [J]. Remote Sens Lett 11(5):416–425

    Article  Google Scholar 

  20. Wu ZZ, Wan SH, Wang XF et al (2020) A benchmark data set for aircraft type recognition from remote sensing images [J]. Appl Soft Comput 89(1):106132

    Article  Google Scholar 

  21. Wang Q, Liu S, Chanussot J et al (2018) Scene classification with recurrent attention of VHR remote sensing images [J]. IEEE Trans Geosci Remote Sens 57(2):1155–1167

    Article  Google Scholar 

  22. Batool M, Jalal A, Kim K (2020) Telemonitoring of daily activity using accelerometer and gyroscope in smart home environments [J]. J Elect Engin Technol 15(6):2801–2809

    Article  Google Scholar 

  23. Khan MA, Sharif M, Akram T et al (2020) Hand-crafted and deep convolutional neural network features fusion and selection strategy: an application to intelligent human action recognition [J]. Appl Soft Comput 87:105986

    Article  Google Scholar 

  24. Chen C, Gong W, Chen Y et al (2019) Learning a two-stage CNN model for multi-sized building detection in remote sensing images [J]. Remote Sens Lett 10(2):103–110

    Article  MathSciNet  Google Scholar 

  25. Žemgulys J, Raudonis V, Maskeliūnas R et al (2018) Recognition of basketball referee signals from videos using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) [J]. Procedia computer science 130:953–960

    Article  Google Scholar 

  26. Sosale B, Aravind SR, Murthy H et al (2020) Simple, Mobile-based artificial intelligence algorithm in the detection of diabetic retinopathy (SMART) study [J]. BMJ Open Diabetes Res Care. https://doi.org/10.1136/bmjdrc-2019-000892

    Article  Google Scholar 

  27. Li Y, Huang Q, Pei X et al (2020) RADet: Refine feature pyramid network and multi-layer attention network for arbitrary-oriented object detection of remote sensing images [J]. Remote Sens 12(3):389

    Article  Google Scholar 

  28. Wang L, Ding L, Liu Z et al (2020) Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning [J]. Br J Ophthalmol 104(3):318–323

    Article  Google Scholar 

  29. Wu X, Huang Y, Liu Z et al (2019) Universal artificial intelligence platform for collaborative management of cataracts [J]. Br J Ophthalmol 103(11):1553–1560

    Article  Google Scholar 

  30. Lv Z, Li X, Lv H et al (2019) BIM Big Data Storage in WebVRGIS[J]. IEEE Trans Industr Inf 16(4):2566–2573

    Article  MathSciNet  Google Scholar 

  31. Xu T, Tang L (2021) Adoption of machine learning algorithm-based intelligent basketball training robot in athlete injury prevention [J]. Front Neurorobot 14:117

    Article  Google Scholar 

  32. Minkin VA, Kachalin AN (2020) Dependence of emotional parameters values on inter-frame difference accumulation in vibraimage system[J]. Modern Psychophysiol Vibraimage Technol 12:139–145

    Google Scholar 

  33. Guo J, Zhang H, Zhen D et al (2020) An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression[J]. Measurement 151:107240

    Article  Google Scholar 

  34. Sun J, Ding E, Li D et al (2020) Continuously adaptive mean-shift tracking algorithm based on improved gaussian model[J]. J Engin Sci Technol Rev. https://doi.org/10.25103/jestr.135.07

    Article  Google Scholar 

  35. Mao W, Xie H, Tan Z et al (2020) High precision indoor positioning method based on visible light communication using improved Camshift tracking algorithm[J]. Optics Communications 468:125599

    Article  Google Scholar 

  36. Abbasi M, Khosravi MR (2020) A robust and accurate particle filter-based pupil detection method for big datasets of eye video[J]. J Grid Comput 18(2):305–325

    Article  Google Scholar 

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  1. School of Sports Training, Shenyang Sport University, Shenyang, 110102, China

    Ning Liu & Pai Liu

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  1. Ning Liu
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  2. Pai Liu
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Correspondence to Pai Liu.

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Liu, N., Liu, P. Goaling recognition based on intelligent analysis of real-time basketball image of Internet of Things. J Supercomput 78, 123–143 (2022). https://doi.org/10.1007/s11227-021-03877-3

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