基于卷积神经网络的时空权重姿态运动特征提取算法

doi:10.3969/j.issn.0255-8297.2021.04.007

Abstract

Abstract: In traditional attitude motion feature extraction process, there is the problem of low efficiency. As to this, a temporal and spatial weight attitude motion feature extraction algorithm based on convolutional neural network (CNN) algorithm is proposed in this paper. First, from selected motion spatio-temporal samples, corresponding spatio-temporal motion keyframes are extracted and output in the form of static images. Second, initial moving images are preprocessed by using moving object detection, image enhancement and other measures. Then the motion feature is vectorized by CNN, and the adaptive interpolation method of space-time weight is used to reduce the error of motion edge detection. Finally, feature extraction of attitude motion is realized from two aspects of attitude edge feature and space-time feature of attitude motion, and extraction results are output. Compared with the traditional algorithm, experimental results show that the proposed algorithm improves the number of effective features.

Key words: convolutional neural network (CNN), weight of time and space, movement posture, movement characteristic, feature extraction algorithm

CLC Number:

TP391

ZHENG Changliang, PANG Ming. Spatial-Temporal Weight Attitude Motion Feature Extraction Algorithm Using Convolutional Neural Network[J]. Journal of Applied Sciences, 2021, 39(4): 594-604.

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Spatial-Temporal Weight Attitude Motion Feature Extraction Algorithm Using Convolutional Neural Network

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