Human Motion Model Construction Based on Gene Expression Programming

Abstract

In this paper, we propose a novel method based on Gene Expression Programming (GEP) to construct human motion model. Our approach better describes human motion features, which can be applied to improve the accuracy of human behavior recognition. On one hand, this method combines Genetic Algorithm (GA) and Genetic Programming (GP), and overcomes the limitation of traditional high-dimension function approaching method, realizing the generalization of Gene Expression Programming (GEP) on Function Mining. On the other hand, it implements the human motion capture technique of Kinect sensor, interpolates data and increases the training data accuracy. In the experiments result, we use GEP to develop human trajectory dynamics model, which has characteristics like encoding and gene structure flexibility that can lead the trajectory simulation error much decline. Given that the result is better than traditional methods and able to maintain most of the human motion features, our human motion model can be applied to human behavior analysis area and other similar domains.