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Top1. Introduction
When human motion feature recognition is performed in a computer three-dimensional visual space, a three-dimensional motion planning method is used to analyze the human motion characteristics, and a motion characteristic analysis model of the human motion is established. In this way, a spatial planning model of human motion can be realized, and feature sampling and information recognition of human motion images can be performed in a computer vision environment, thereby guiding the accuracy of human motion images and improving the ability to guide and correct human motion (Sun et al.,2019). The reliability of human motion recognition is greatly affected by the spatial distribution of the image. The uneven distribution of the area will reduce the peak signal noise of the image and affect the reliability of the recognition. Therefore, the use of image processing and computer three-dimensional vision analysis methods to reconstruct the three-dimensional vision of human motion images is of great significance in the guidance of human motion, and the related research on human motion recognition methods has received great attention (He et al.,2018; Zheng et al.,2018).
The 3D visual reconstruction and recognition of human motion images is based on the image reliability enhancement processing.
Knowledge graph is an information enhancement method with better current application performance. The knowledge graph is called knowledge domain visualization or knowledge domain mapping map in the library and information industry. It is a series of various graphics showing the relationship between knowledge development process and structure. It uses visualization technology to describe knowledge resources and their carriers, excavating, analyzing, constructing, Map and display knowledge and their interrelationships. It has the advantages of narrowing the search scope and improving the search content and breadth. Therefore, we proposes a knowledge graph-based human motion recognition reliability enhancement algorithm. It is expected that the image enhancement can be achieved through the extraction and reconstruction of the knowledge graph set, so as to find a more effective human motion recognition method. Contribute to the development of computer vision. The results show that the overall performance of the algorithm in this paper is better and has certain advantages. The first part of this paper introduces the introduction and related work, the second part introduces the algorithm of this paper, the third part introduces the results and discussion, and finally the conclusion.