Open AccessAPPLICATION OF NONLINEAR DYNAMIC SYSTEM AND TECHNOLOGY IN MOVING TARGET DETECTION DURING SPORTS TRAINING
Author(s) -
Kai Lung Hua,
Ehab Abozinadah,
Raquel Suriá Martínez
Publication year - 2022
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x22400588
Subject(s) - computer science , nonlinear system , convergence (economics) , kalman filter , artificial intelligence , extended kalman filter , tracking (education) , training (meteorology) , filter (signal processing) , machine learning , computer vision , psychology , pedagogy , physics , quantum mechanics , meteorology , economics , economic growth
The aim of this paper is to improve the accurate analysis of the sports training project, research its video data profoundly, and discover deficiencies and summarize experience from the existing videos, thereby promoting China’s sports industry. According to previous works, the existing sports training target detection system has the problem of nonlinear video and low target detection accuracy, which can easily cause target loss or tracking failure. Therefore, the Kalman Filter (KF) is studied in-depth and applied to detect the targets in sports videos. Combined with the Multi-Innovation (MI) theory, an MI-Extended KF (EKF) algorithm model based on nonlinear dynamic technology is proposed. This model can effectively solve the filtering accuracy problem under the strong nonlinear system. Finally, the performance analysis through different datasets has verified the effectiveness of the model proposed further. Results demonstrate that the model proposed can effectively improve the filtering accuracy of nonlinear systems. As the angle module changes, the accuracy of sports recognition also changes. The final accuracy of the model proposed can reach above 96%. The simulation results and convergence are better than other algorithms, which also proves the effectiveness of the model proposed. The results can provide a theoretical basis for the research related to the detection of training sports targets in sports videos.