Skeleton-based behavior detection method, terminal device, and computer storage medium

Abstract

A skeleton-based behavior detection method The invention relates to a terminal device and a computer storage medium, which relate to a limb motion detection technology in the biological detection field, wherein the method comprises the following steps: acquiring original image data; determining a skeleton joint node in the original image data based on an image detection algorithm, and acquiring the joint node coordinates of the skeleton joint node; Converting the joint coordinates based on a preset conversion rule to obtain a joint feature vector; Classifying and recognizing the original imagedata based on the image classification algorithm and the joint feature vector to obtain a behavior detection result can improve the accuracy and robustness of skeleton behavior detection.

Description

technical field [0001] The present application relates to the technical field of computer vision, and in particular to a skeleton-based behavior detection method, terminal equipment and computer storage media. Background technique [0002] Vision-based human motion analysis is a very active research direction in the field of computer vision, which includes motion detection, object classification and tracking, and understanding and recognition of human motion. The relationship between these contents is progressive: the detection of moving human body is the basis of target classification and tracking, and human action recognition can be performed on the basis of solving these basic problems. The selection of motion features directly affects the recognition effect of human action recognition methods. A single feature is often affected by factors such as the appearance of the human body, the environment, and camera settings, and its application range is different, and the recog...

AI Technical Summary

Problems solved by technology

A single feature is often affected by factors such as human appearance, environment, and camera settings, and its application range is different, and the recognition effect is also limited.

At present, the mainstream method is that the computer processes and analyzes the original image or image sequence data collected by the sensor (camera), recognizes the characters in the video, learns and understands the actions and behaviors of the people in it, and generally recognizes the occluded situation. The effect is not good, and there may be shortcomings such as low accuracy and low robustness

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