Human motion tracking method based on deep nuclear information image feature

Abstract

The invention discloses a human motion tracking method based on a deep nuclear information image feature. The human motion tracking method based on the deep nuclear information image feature mainly solves the problems that in human motion tracking of the prior art, features of a video image are not accurately expressed, so that a tracking result is caused to be not accurate. The method comprising the steps: obtaining an articulation point three-dimensional coordinate matrix Y of the video image from a data bank; extracting the deep nuclear information image feature X of the processed video image; serving the deep nuclear information image feature X as an input, serving the three-dimensional coordinate matrix Y, in the video image, of a human body as an output, and learning a regression function by using of gaussian process; learning an obtained regression function by using of the gaussian process, serving a new deep nuclear information image feature X of the video image as an input, and estimating data of three-dimensional poses of a moving body. Compared with an existing human body tracking method, the human motion tracking method based on the deep nuclear information image feature has the advantages of being high in training speed, accurate in express of image features, and capable of being used in motion catching, human-computer interaction, video surveillance, recognition of human body goals and restoration of the three-dimensional poses.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a method for realizing human body motion tracking in the field of computer vision, which can be used in the fields of sports training, animation production, and video monitoring. technical background [0002] Human motion tracking is one of the major hotspots in the field of computer vision in the past two decades. Human motion tracking has been initially applied in many fields such as motion capture, human-computer interaction, and video surveillance, and has great application prospects. Accurately recovering 3D human pose from video sequences and realizing human motion tracking is a long-standing problem in the field of computer vision. The realization of human body motion tracking mainly includes two steps: the first step is to realize the accurate representation of video image features, and the second step is to learn the regression function from video image fe...

AI Technical Summary

Problems solved by technology

However, most of the image feature representations describing the human body are based on contour and edge information, which is not rigorous in theory, and it is difficult to accurately describe the internal information of the image.

These edge-based image feature representation methods also face a major problem: the rapid transformation of the video image often jumps along the edge curve discontinuity, which on the one hand will cause the gray level discontinuity of the closed boundary to be blurred, and on the other hand will cause texture changes. Does not gather along geometric curves

The end result is that the geometric texture direction in the image cannot be effectively represented, and the pose and feature information of the person in it cannot be fully described, resulting in ambiguity and ambiguity in the later motion tracking and pose recovery.

Images