Human body action recognition method

Abstract

The invention provides a human body action recognition method, and relates to the field of action recognition. The method comprises the steps: obtaining feature data of articulation points, and establishing articulation point set distance features, geometrical features and motion features; modeling the spatial and temporal features of the action sequence from multiple angles, and modeling the timesequence information of the action sequence by adopting a one-dimensional time convolution network; through time-space characteristics and time sequence information, carrying out the human body action classification and identification; through designing a plurality of characteristic representations, describing the space and geometric information of different joint points in the same frame, describing the time motion information of joint points between adjacent frames, and better modeling the space-time characteristics of human body motion; meanwhile, employing a one-dimensional time convolution network for modeling time sequence information, carrying out the human body motion classification in combination with constructed characteristics, and obtaining an ideal classification and recognition effect.

Description

technical field [0001] The present disclosure relates to the field of action recognition, in particular to a human action recognition method. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] Human motion behavior recognition has important application value in elderly care, physical therapy rehabilitation, animation game production, security monitoring, factory man-machine collaboration, etc. [0004] The inventors found that at present, depth maps and human skeleton data are used for human behavior recognition, but there are still certain drawbacks. The traditional human behavior based on depth maps establishes depth map datasets from multiple perspectives and extracts a large number of features. The computational complexity of the learning model increases rapidly, resulting in slower practical application. In addition, due to the differ...

AI Technical Summary

Problems solved by technology

[0004] The inventors found that at present, depth maps and human skeleton data are used for human behavior recognition, but there are still certain drawbacks. The traditional human behavior based on depth maps establishes depth map datasets from multiple perspectives and extracts a large number of features. The computational complexity of the learning m

Images