Automatic labeling method for human joint based on monocular video

Abstract

The invention provides an automatic labeling method for a human joint based on a monocular video. The automatic labeling method comprises the following steps: detecting a foreground and storing the foreground as an area of interest; confirming an area of a human body, cutting a part and obtaining an outline of sketch; obtaining a skeleton of the human body and obtaining a key point of the skeleton; utilizing a relative position of face and hands to roughly estimate the gesture of human body; and automatically labeling the point of human joint. During an automatic labeling process, the sketch outline information, skin color information and skeleton information from sketch of human body are comprehensively utilized, so that the accuracy for extracting the joint point is ensured. According to the automatic labeling method provided by the invention, the accurate and efficient cutting for the part of human body is performed, the gesture information of each limb part is obtained and a beneficial condition is supplied to the next operation for obtaining and treating feature vectors of human body.

Description

technical field [0001] The invention belongs to the field of computer vision and relates to an automatic initialization method in motion capture, which can be used for posture estimation and action recognition of human bodies. Background technique [0002] Research on vision-based markerless motion capture technology began in the 1980s. Marker-free motion capture technology integrates the research content of computer vision, computer graphics, image processing, human kinematics and artificial intelligence. It is a hot field interdisciplinary and extremely challenging. Human motion capture technology has strong practical value and can be widely used in various fields, mainly including intelligent monitoring system, new human-computer interaction, medical diagnosis and analysis, film and animation production, game production, virtual reality, content-based video sequence indexing and retrieval, athlete auxiliary training, etc. For example, in the current human-computer inter...

AI Technical Summary

Problems solved by technology

However, human skeleton reconstruction faces the problem

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