STEREO VISION INFORMATION SYSTEM USING MEDIAN THEOREM AND ATTITUDE COMPENSATION WITH NONLINEAR DIFFERENTIAL EQUATIONS

Yuejin Zhang, Guanxiang Yin, Mengqiu Ye, Qi Liu, Botao Tu, Guanghui Li and Aiyun Zhan
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Yuejin Zhang: School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, P. R. China
Guanxiang Yin: School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, P. R. China
Mengqiu Ye: School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, P. R. China
Qi Liu: School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, P. R. China
Botao Tu: School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, P. R. China
Guanghui Li: School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, P. R. China
Aiyun Zhan: School of Information Engineering, East China Jiaotong University, Nanchang, Jiangxi, P. R. China

FRACTALS (fractals), 2022, vol. 30, issue 02, 1-12

Abstract: Using computer vision technology to obtain and analyze biomechanical information is an important research direction in recent years. However, the linear model in the computer vision system cannot accurately describe the geometric relationship of the camera imaging, so it is difficult to realize human posture recognition in high-precision mechanics information. Therefore, how to improve the recognition accuracy is very important. In this paper, we apply nonlinear differential equations to stereo computer vision (SCV) information systems. And based on the median theorem, a nonlinear posture recognition and error compensation algorithm based on BP neural network is proposed to reduce the recognition error. The test set uses the Leeds Motion Pose (LSP) dataset to verify the performance of the algorithm. Experimental results show that the compensated median filter of BP neural network can eliminate glitches in attitude data. Superimposing the output attitude error compensation value with the attitude estimation value can greatly reduce the root-mean-square error of the attitude angle. The result of gesture recognition is closer to reality. Compared with traditional algorithms, the cyclomatic complexity of the proposed BP neural network algorithm has a much lower growth rate in high-order calculations, which indicates that the proposed BP neural network algorithm is more concise and scalable.

Keywords: Nonlinear Differential Equation; Median Theorem; The BP Neural Network; Stereo Computer Vision; Attitude Compensation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22400734

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