Human Pose Recognition Method Based on Skeletal Feature Point Extraction

Abstract

The invention discloses a human body posture recognition method based on skeleton feature point extraction, which is mainly used to solve the problem of human body posture recognition by using a single camera. The implementation steps are: (1) video input; (2) preprocessing; (3) extracting rough bones; (4) extracting bone feature points; (5) bone feature point classification; (6) gesture recognition; (7) output Gesture recognition results. The present invention uses the shortest distance from the pixel point of the outline map to the edge to obtain the thick skeleton, then uses the distance to the front chest node to calculate and classify the skeleton feature points, and then performs posture judgment according to the relative position information of the skeleton feature points and the front chest node. It can avoid a large number of calculations to judge human body movements when there is only a video stream input from a single camera. The invention has the advantages of small calculation amount, low hardware requirement, high accuracy rate and strong adaptability.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a human body posture recognition method based on bone feature point extraction in the field of computer vision. The invention can be used in an intelligent monitoring system to identify abnormal behaviors of human bodies in the environment, and can also be used in conventional cameras to identify human body actions to realize remote control and human-computer interaction. Background technique [0002] At present, human action recognition is mainly based on complex texture calculations or requires the support of other devices besides a single camera. If the action is recognized through texture calculation, most of the color images or grayscale images need to be sampled, learned, and matched. In the application, there is a very obvious problem: the amount of calculation is large, and most of them require long-term calculations due to performance limitations. Obtaini...

AI Technical Summary

Problems solved by technology

If the action is recognized through texture calculation, most of the color images or grayscale images need to be sampled, learned, and matched. In the application, there is a very obvious problem: the amount of calculation is large, and most of them require long-term calculations due to performance limitations. Obtaining results that lead to inability to complete timely remote control

If it is supported by other devices other than a single camera, there are several problems in the application: 1) The cost increases. Whether it is adding a camera or adding an infrared camera and a wearable wireless remote sensor, the cost of the device needs to be greatly increased; 2) The usage scenarios are limited, infrared cameras are not suitable for multi-person situations such as streets, and wearable wireless remote sensors are not suitable for outdoor scenes

The disadvantage of this method is that the number of motion recognition is limited by using the preset motion map, and the difference in shooting angle or contour map extraction will lead to low recognition rate.

The disadvantages of this method are: the amount of calculation for judging actions through feature extraction is large, and the assistance of motion sensors is required to increase the application cost and limit the application scenarios.

The shortcomings of this method are: poor anti-noise data ability; in the case of unsatisfactory extraction of human body contours, it will lead to a high error rate in the connection of bone feature points, so that the correct human body posture cannot be judged

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