Abstract
A human motion recognition method based on Kalman random forest algorithm model is proposed to improve the accuracy and efficiency of tracking algorithm aiming at the multi-degree-of-freedom problem of traditional human motion recognition and tracking. Firstly, a simple explicit manifold space model of human motion parameters is established. Reconstruction of three-dimensional image features for human motion requires accurate identification of human behavioral features. The human motion process is highly random, and the sudden change in motion amplitude causes a sudden change in the number of shape-based parameters in the three-dimensional feature. The compact low-dimensional model simplifies the difficulty of human motion parameters contour tracking; Secondly, Kalman random forest algorithm is used to estimate the global transformation of action and the different distance measures of body action in each sub-state by using action characteristics through boundary approximation and action sequence updating, so as to reduce the computational complexity of the algorithm; Finally, the single shape change and the dynamic action in motion are modeled, and the dynamic tracking of human motion is realized through the linear combination of human motion shape and style factor decomposition.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call