Human body posture recognition method and device

Abstract

The invention provides a human body posture recognition method and device. The method comprises the steps of providing a to-be-processed image; Performing target detection processing on the to-be-processed image to obtain one or more first head detection frames; Performing key point calculation processing on the first head detection frame to obtain a plurality of pieces of first head key point information; Performing recognition processing on the to-be-processed image from bottom to top to obtain one or more pieces of second human body posture information; Extracting second head key point information and first trunk key point information from the second human body posture information; Fusing the first head key point information and the second head key point information to obtain fused third head key point information; And taking the third head key point information and the first trunk key point information as a human body posture recognition result. According to the invention, the accuracy of human body posture recognition can be improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a human body posture recognition method, a human body posture recognition device, electronic equipment and a computer-readable storage medium. Background technique [0002] In recent years, computer vision technology based on artificial intelligence has developed rapidly. As an important direction, human gesture recognition has great application prospects in many fields such as robot interaction, games, animation, behavior recognition, and security monitoring. [0003] Human body gesture recognition technology in the prior art mainly includes two categories: [0004] One is a top-down recognition method, that is, first locate the approximate position of the human body, and then perform specific recognition of the posture. The most commonly used method is to first use the target detection method to obtain the position frame of each person in the image, and then detect the...

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Problems solved by technology

[0006] However, both of the above two types of methods have the problem of low recognition accuracy: in the first type of top-down method, the individual is checked first, and then the gesture is recognized. The steps are separated from each other and have no internal connection, which makes gesture recognition heavily dependent on The results of the previous step detection and positioning; the second type of bottom-up method first detects all key points of the human body, and then connects these key points through a clustering algorithm to form a complete individual. The key points are very unsatisfactory when the human body is relatively close. In many cases, the key points of one person will be connected to another human body, and this method cannot find body parts from global features.

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