Recognizing the take-off action pattern of basketball players based on fuzzy neural network system

Abstract

The description and recognition of target image shape is an important content in the field of computer vision. This research mainly discusses the pattern recognition of the take-off action of basketball players based on the fuzzy neural network system. In this study, the key points of the human body are enhanced by introducing a 3D posture fuzzy neural network. In order to visualize the depth map, it is processed in pseudo-color. The network architecture is divided into two modules, namely a 2D human body key point detection module and a 3D posture estimation network module. Among them, the function of the 2D human body key point detection module is to detect the position information of the human body key points in a single color image. Each joint point is represented by a score map. The largest score map predicts that the key points in the image exist, and all the score maps of are merged into a set, which can uniquely represent the 2D key points of the human body. The function of the 3D pose estimation fuzzy neural network module is to take the depth map and the coordinate set of the 2D human body key points of the previous module as input data. Through a series of 3D convolution operations, the coordinates of the 3D human body key points are obtained as the output of the module a complete 3D human pose. The designed fuzzy neural network classifier also uses the backward propagation algorithm of the gradient descent principle to modify its connection weights. In the application, it is also divided into two stages, namely the supervised learning stage and the classification stage of the unknown data. It can also be adapted to data with fuzzy nature in classification, and its output is no longer the result of a single classification, but various degrees of membership. The detection accuracy of the right ankle was 93.47%, and the detection accuracy of the right knee was 88.99%. The fuzzy neural network system designed in this research has a good effect on pattern recognition of take-off actions.