Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

Abstract

To improve the extraction accuracy of knee bending motion in ball motion image and reduce the extraction distance error and time consumption, a knee bending motion extraction algorithm using visual sensor is proposed. The visual sensor model is constructed based on the ball motion frame image, the trigger data is output through differential and logical judgment, and these data are normalized to generate the visual sensor sample set of the ball motion frame image. The sample set is used as the input of the convolution neural network (CNN) and the sample basis of the motion energy model. The CNN extracts the features of the sample set in the convolution layer, the motion energy model is combined with the local binary pattern to extract the features of the sample set, the weighted summation method is used to fuse the two features, and the Softmax classifier is used to classify and extract the knee bending motion. The results show that the proposed algorithm has good ball motion image collection effect, the knee bending motion extraction accuracy is always maintained at about 98%, the distance error is low, and the time consumption of ball motion feature extraction is only 2.65 s, which has high application value in the field of sports.