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Particle filter algorithm based spatial motion tracking of football landing location

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Abstract

In order to improve the accuracy of the tracking of football landing location, a football landing location and spatial motion tracking system based on APRBA multi-camera was proposed. First of all, the study of football movement locus model was conducted. Without considering the speed limit and direction restriction, Bayesian state evolution model was constructed, and based on path loss model, the moving target positioning observation model was established. Secondly, by using the ultrasonic food source localization of the proposed APRBA algorithm and the next target prediction, the particle filter algorithm was improved to realize the effective improvement of target tracking accuracy. Finally, the effectiveness of the proposed target tracking algorithm was verified in the tracking experiment of the moving target model.

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References

  1. Arunkumar N, Ramkumar K, Venkatraman V, Abdulhay E, Fernandes SL, Kadry S, Segal S (2017) Classification of focal and non focal EEG using entropies. Pattern Recogn Lett 94:112–117

    Article  Google Scholar 

  2. Chan JW, Zhang Y, Uhrich KE (2015) Amphiphilic Macromolecule Self-Assembled Monolayers Suppress Smooth Muscle Cell Proliferation. Bioconjug Chem 26(7):1359–1369

    Article  Google Scholar 

  3. Du X, Chen L, Huang D, Peng Z, Zhao C, Zhang Y, Zhu Y, Wang Z, Li X, Liu G (2017b) Elevated Apoptosis in the Liver of Dairy Cows with Ketosis. Cell Physiol Biochem 43(2):568–578

    Article  Google Scholar 

  4. Malarkodi MP, Arunkumar N, Venkataraman V (2013) Gabor wavelet based approach for face recognition. Int J Appl Eng Res 8(15):1831–1840

    Google Scholar 

  5. Pan W, Chen S, Feng Z (2013) Automatic Clustering of Social Tag using Community Detection. Applied Mathematics & Information Sciences 7(2):675–681

    Article  Google Scholar 

  6. Stephygraph LR, Arunkumar N (2016) Brain-actuated wireless mobile robot control through an adaptive human-machine interface. Adv Intell Syst Comput 397:537–549

    Google Scholar 

  7. Stephygraph LR, Arunkumar N, Venkatraman V (2015) Wireless mobile robot control through human machine interface using brain signals. 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, ICSTM 2015 - Proceedings, art. no. 7225484, pp. 596-603

  8. Zhang Y, Li Q, Welsh WJ, Moghe PV, Uhrich KE (2016) Micellar and Structural Stability of Nanoscale Amphiphilic Polymers: Implications for Anti-atherosclerotic Bioactivity. Biomaterials 84:230–240

    Article  Google Scholar 

  9. Zhao Y, Hu YH, Liu J (2017) Random Triggering-Based Sub-Nyquist Sampling System for Sparse Multiband Signal. IEEE Trans Instrum Meas 66(7):1789–1797

    Article  Google Scholar 

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Correspondence to Haixiong Ye.

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Ye, Z., Ye, H. Particle filter algorithm based spatial motion tracking of football landing location. Multimed Tools Appl 79, 5053–5063 (2020). https://doi.org/10.1007/s11042-018-6307-8

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