摘要
At present,Animal Exercise courses rely too much on teachers’subjective ideas in teaching methods and test scores,and there is no set of standards as a benchmark for reference.As a result,students guided by different teachers have an uneven understanding of the Animal Exercise and cannot achieve the expected effect of the course.In this regard,the authors propose a scoring system based on action similarity,which enables teachers to guide students more objectively.The authors created QMonkey,a data set based on the body keys of monkeys in the coco dataset format,which contains 1,428 consecutive images from eight videos.The authors use QMonkey to train a model that recognizes monkey body movements.And the authors propose a new non-standing posture normalization method for motion transfer between monkeys and humans.Finally,the authors utilize motion transfer and structural similarity contrast algorithms to provide a reliable evaluation method for animal exercise courses,eliminating the subjective influence of teachers on scoring and providing experience in the combination of artificial intelligence and drama education.
