Human Pose Recognition Method in 2D Video Image

Abstract

The invention discloses a human body pose recognition method in a two-dimensional video image, comprising the following steps: grouping the original video images according to the size of the scale; calculating a sampling image of a specified scale for each group of images, and calculating the Calculate the HOG; use the HOG prediction of a sampled image in each group to calculate the HOG corresponding to other specified scale sampled images in the group; according to the obtained multi-scale HOG, combined with the trained SVM classifier to detect the different scales of the original video image The human body target area; use the trained random forest classifier to classify the pixels of the detected human body target area, and determine the body part area in the human body target area; connect each body part to form a human body contour, and realize human body posture recognition. By applying the method of the present invention, the calculation speed of the multi-scale bottom layer features is accelerated without reducing the detection precision, and the gesture recognition speed and precision are improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a human body posture recognition method in two-dimensional video images. Background technique [0002] Human gesture recognition can be applied to human activity analysis, human-computer interaction, and visual surveillance, and it is a hot issue in the field of computer vision in the near future. Human pose recognition refers to detecting the position of each part of the human body from the image and calculating its direction and scale information. The results of pose recognition are divided into two-dimensional and three-dimensional cases, and the estimation methods are divided into model-based and model-free approaches. [0003] A Chinese patent application with a publication number of CN101350064A discloses a method and device for estimating a two-dimensional human pose. This method first detects the human body area in the two-dimensional image and deter...

AI Technical Summary

Problems solved by technology

[0011] First, using existing methods such as optical flow method, inter-frame difference method, and background phase difference to detect human body areas in two-dimensional images, there are problems such as illumination changes, background dynamic changes, and optical flow multi-scale calculation speed. , which often leads to a large error in the detected human body area, burying hidden dangers for the subsequent human body part detection algorithm, which will lead to the failure of the overall algorithm;

[0012] Second, using the face detection method to locate the head area will have the problem of partial or complete occlusion of the face, resulting in the inability to detect. Moreover, face detection algorithms often only have high detection accuracy for frontal faces, and have high detection accuracy for side faces. The face effect is poor;

[0013] Third, the template matching method for human body part recognition and positioning will cause low accuracy problems. The human body parts shown in the video image will be due to factors such as scale changes and different clothing, which will cause the accuracy of the matching recognition algorithm to deteriorate, resulting in human body parts. Part positioning error makes the whole algorithm invalid

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