特价之四

Title：
Machine learning based method for recognition of image to capture motion behavior for sports training
（基于机器学习的图像识别方法捕捉运动训练运动行为）
Abstract：
There is a need to devise intelligent methods for motion based image classification to produce accurate results for the correct judgement. The existing methods cannot use the image block classification method to recognize the motion behavior of antagonistic sports training and it is the need of the hour to devise newer methods for the same. The recognition result is not ideal by using the traditional methods. This paper proposes an image block classification method for antagonistic sports training behavior recognition. It classifies the noise sources, quantifies the influence degree of each noise source effectively and adjusts the noise factor in the de-noising method for different noise sources and the specific weight of different noise sources. The adaptive de-noising of different noise types of antagonistic sports training image sequence is realized. By using the combination of key frame template selection method and image segmentation method for behavior representation, the features are extracted well. The feature extraction is done on the image region according to the proportion of the number of foreground pixels in each block of the template to the number of pixels in the block. The feature vector is used to form the template. Aiming at the behavior representation and feature extraction based on the features of skeleton joint points, image block classification is used to extract the coordinates of skeleton joint points during human movement. K-means clustering is used to transform them into symbol sequence to represent behavior features which is used for antagonistic sports training behavior recognition. Simulation results show that the proposed method can obtain ideal recognition results and it outperforms the existing methods.

期刊：
soft computing
期刊分类：计算机科学，人工智能；跨学科应用
国际刊号：1432-7643
分区：JCR2区/中科院3区
影响因子：3.731
检索：SCIE
出版版面：专刊
出版社：SPRINGER