Human movement tracking method based on combination of production and discriminant

Abstract

The invention discloses a human movement tracking method based on combination of production and discriminant. The human movement tracking method is mainly used for solving the problem of inaccurate tracking result of the human movement in the prior art. The implementation steps are as follows: building a human skeleton model; pre-treating a video picture to obtain a detection joint; extracting bandlet 2 characteristic of the video picture; inputting the extracted bandlet 2 characteristic and predicting the human gesture by using double-Gauss; initializing the human skeleton model according to the detected joint; structuring 2D and 3D similarity functions between the joint predicted by the double-Gauss and the detected joint; minimizing the similarity function under the restriction of the skeleton length to obtain a group of human gesture; and selecting the state with the minimum Euclidean distance to the former frame skeleton from the obtained human gesture as the best movement gesture of the current frame. Compared with the existing human tracking method, the human movement tracking method based on combination of production and discriminant has the advantages of high accuracy of tracking result and high stability, and can be applied to the medical treatment, the physical training, the animation production and an intelligent monitoring system.

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 adopts a multi-objective optimization method to realize human body motion tracking and three-dimensional attitude estimation, and can be used in fields such as sports training and animation production . Background technique [0002] The main task of human motion tracking is to detect the outline of the human body from the video image, and then locate the joint points of the human body. Since the current video image is the projection of the human body contour in the 3D scene on the 2D image, a lot of depth information is lost, and during the movement of the human body, the self-occlusion phenomenon of the limbs of the human body often occurs, and the video image has ambiguity, which makes It is difficult to recover human motion pose from unlabeled monocular videos. However, du...

AI Technical Summary

Problems solved by technology

The disadvantage of the method disclosed in this patent application is that it can only track the human body of the athlete in a fixed scene, and the similarity of the appearance model is not enough to accurately track the human body posture

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