Complexity Graph-Based Multilabel Classification Method of Human Action in Rope Skipping Scene

Abstract

Aiming at the problem of insufficient accuracy of multilabel classification of human action at present, a multilabel classification method of human action in the rope skipping scene is proposed. It realizes feature recognition and classification by collecting human action features in the scene of skipping rope movement and uses RNN to optimize the human action feature recognition algorithm. On the basis of feature recognition, the characteristics of human movement in the rope skipping scene are classified, the confidence map of the key point position is obtained by using the Gaussian modeling method, and the action multilabel classification is realized. Finally, experiments show that the multilabel classification method of human action in rope skipping scene has high accuracy and fully meets the research requirements.

Analysis of Children’s Sports Heuristic Teaching Based on Deep Learning

“…In arti cial intelligence science, the multi-label limb posture analysis problem in children's physical education heuristic teaching can be transformed into multi-label learning problem. Based on the principle of deep learning, through the combination of multi-label limb posture analysis in children's sports heuristic teaching, this paper designs a heuristic teaching algorithm for children's sports, so as to solve the problems in the process of children's sports heuristic teaching [1][2][3][4][5][6][7][8][9][10].…”

Retracted: Complexity Graph-Based Multilabel Classification Method of Human Action in Rope Skipping Scene

“…Security and Communication Networks has retracted the article titled "Complexity Graph-Based Multilabel Classifcation Method of Human Action in Rope Skipping Scene" [1] due to concerns that the peer review process has been compromised.…”