Skeleton graph regression-based three-dimensional human body posture estimation method

Abstract

The invention provides a skeleton graph regression-based three-dimensional human body posture estimation method. The method mainly comprises the steps of segmentation, regression and matching. The process of the method comprises the following steps of firstly, giving an RGB image of a person, generating a foreground skeleton graph and a background skeleton graph through the deconvolution process of an encoder-decoder architecture for each configuration of a cutting scale and a width ruler, respectively feeding a skeleton mapping into a separate regression network, adopting the skeleton graphsas the input, outputting an assumption of three-dimensional gestures so as to generate a plurality of three-dimensional assumptions, and finally selecting an assumption minimum in projection error fortwo-dimensional joint detection as a final output in order to match a two-dimensional observation value. According to the invention, a regression network is independently trained based on skeleton graphs. When the regression network is combined with a plurality of assumptions, the better estimation effect can be achieved on indoor and outdoor data sets. The influences on results caused by illumination, shielding and the like is greatly reduced. The performance of the attitude estimation is improved to a great extent.

Description

technical field [0001] The invention relates to the field of attitude estimation, in particular to a method for estimating three-dimensional human body attitude based on skeleton graph regression. Background technique [0002] Automatically extracting human body posture information from human body images or video sequences is one of the research hotspots in the field of machine vision. Using the method of human body posture estimation, the computer system can extract the posture of the human body according to the video information captured by the camera system, and then analyze and judge the behavior of the human body according to the change of posture. If this method can be applied to traditional video surveillance equipment, it will help the machine realize the function of analyzing video content, and prompt supervisors to stop suspicious or dangerous behaviors in time by identifying abnormal or dangerous behaviors of people in the video. Greatly improve the use efficienc...

AI Technical Summary

Problems solved by technology

However, the traditional 3D human pose estimation faces the problem of insufficient training data. At the same time, due to the influence of lighting, occlusion, etc., the pose estimation effect is often not ideal.

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