Human body posture recognition method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a human body posture recognition method, device and equipment and a storage medium. The method comprises the steps of obtaining a to-be-identified image; wherein the to-be-recognized image comprises imaging information of a human body; performing human body detection on the to-be-recognized image to obtain at least one part of human body information; performing multi-scale recognition on the human body posture key points in the to-be-recognized image to obtain multi-scale key points; performing multi-scale fusion on the multi-scale key points to obtaintarget key points; distributing the target key points to each part of human body information to obtain human body posture information, and completing human body posture recognition; wherein the humanbody posture information is a set formed by the posture of each part of human body information. Through the embodiment of the invention, the accuracy of human body posture recognition can be improved.

Description

technical field [0001] The invention relates to computer vision technology in the field of artificial intelligence, in particular to a human body gesture recognition method, device, equipment and storage medium. Background technique [0002] Artificial Intelligence (AI) is a new science and technology that researches and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. In recent years, with the development of artificial intelligence, computer vision technology based on artificial intelligence has also developed rapidly. Human body gesture recognition, as an important method, has a large application in many fields such as smart supermarkets, security and monitoring. Application prospect. [0003] Generally speaking, when performing human pose recognition, the key points in the image to be recognized are usually detected first, and then the key points are assigned to each person, and then the pose inf...

AI Technical Summary

Problems solved by technology

However, in the above-mentioned process of human body posture recognition, since the acquisition of key points is obtained from the information output from the last layer of the network model, and in the process of processing from the first layer to the last layer of the network model, it is necessary to sequentially process the images to be recognized. Sampling operation, there is pixel loss; therefore, the accuracy of the key points is low, so when the human body posture recognition is realized according to the key points, the accuracy of the recognition result is low

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