基于卷积神经网络的时空权重姿态运动特征提取算法

在传统姿态运动特征提取过程中存在有效提取效率低的问题，于是提出了基于卷积神经网络（convolutional neural network，CNN）算法的时空权重姿态运动特征提取算法。针对所选择的运动时空样本，提取相应的时空运动关键帧并以静态图像的形式输出；采取运动目标检测、图像增强等多项措施完成初始运动图像的预处理工作；借助CNN将运动特征矢量化；采用时空权重自适应插值方法减少运动边缘检测误差，从姿态边缘特征和姿态运动时空特征两方面实现姿态运动特征提取，并输出提取结果。与传统算法进行对比实验的结果表明，所提出的算法在有效特征数量方面得到了提升。

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

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.