Human body pose estimation method and mobile terminal

Abstract

The invention discloses a human body pose estimation method. The human body pose estimation method is suitable for marking human body key points in the video to be processed so as to extract the humanbody pose information, and is suitable for application in a mobile terminal. The human body pose estimation method includes the steps: acquiring the video to be processed, and determining whether thekey point position of the previous image frame of the current image frame exists; if the key point position of the previous image frame does not exist, detecting the human body position in the current image frame, and generating a target frame containing the human body position; if the key point position of the previous image frame exists, according to the human body key point position marked inthe previous image frame, generating a target frame corresponding to the current image frame; performing scale transformation on the generated target frame to generate a human body image with the preset size; inputting the human body image with the preset size into a preset pose estimation model so as to output the image on which the human body key point position is marked; and according to the marked key point position, estimating the human body pose. The invention also discloses a corresponding mobile terminal.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a human body pose estimation method and a mobile terminal. Background technique [0002] Human body pose estimation refers to the process of labeling the key points of the human body in the image or video, and then extracting the basic pose information of the human body, such as figure 1 As shown in , the dots represent the marked key points of the human body, and the basic posture information of the human body can be obtained according to the marked key points of the human body. Commonly used key points of the human body (that is, human body joint points or skeletal points) include the head, neck, shoulders, elbows, wrists, hips, knees, and ankles. Human pose estimation is to identify the positions of these key points on the human body in the image video. [0003] Existing human pose estimation algorithms can be divided into traditional vision methods and deep learnin...

AI Technical Summary

Problems solved by technology

[0004] However, in actual scenes, due to the different proportions of the human body in different images or videos, different clothing, occlusion relations and complex shooting angles, it is often difficult to estimate the human body pose quickly and effectively.

In addition, in addition to the diversity of target appearance, the efficiency of algorithm operation is also an issue that needs to be considered

The current pose estimation algorithm cannot meet the requirements of mobile applications in terms of computational efficiency.

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