Image Feature Representation and Human Movement Tracking Method Based on the Second Generation Striplet Transform

Abstract

The invention discloses an image feature representation and human body motion tracking method based on the second-generation strip wave transformation, which mainly solves the problem that the existing feature extraction is based on the human body contour and edge ambiguity, and cannot reflect the geometric flow direction and texture of the internal image. question. The implementation process is: input the video image to be processed, extract the frame diagram of human body parts; perform two-dimensional multi-scale wavelet transformation on the image; use quadtree division and bottom-up fusion rule to find the optimal geometric flow direction; quantify the optimal The geometric flow direction signal is transformed by one-dimensional wavelet and reorganized into two-dimensional form to obtain the Bandelet2 coefficient matrix; the maximum geometric flow statistical feature is extracted as the final image feature representation; the mapping relationship between image features and three-dimensional motion data is learned through the regression process, and the new Predict and recover 3D pose from training video images. The invention has the advantages of fast tracking, accurate result, small amount of calculation, reduced blurring of image representation, and can be used for human target recognition and three-dimensional posture reconstruction.

Description

technical field [0001] The invention belongs to the technical field of computer vision and video image processing, and mainly relates to a video image feature texture representation method, specifically a second-generation strip wave transform image feature representation and human body motion tracking method, which is used for video human body motion tracking and three-dimensional Posture recovery. Background technique [0002] Video human motion tracking is one of the major hotspots in the field of computer vision in the past two decades. People are the core content and reflect the core semantic features of images. This type of technology has been initially applied in many fields such as motion capture, human-computer interaction, and video surveillance, and has great application prospects. The understanding and interpretation of video human motion tracking belongs to the category of video image processing, and also involves many disciplines such as pattern recognition, m...

AI Technical Summary

Problems solved by technology

However, most of the image feature representations describing the human body are based on contour and edge information, which is not strict in theory, and it is difficult to better describe the internal information of the image.

At the same time, a major problem faced in such edge-based methods is that fast image changes often do not correspond to discontinuity jumps along edge curves, which on the one hand lead to blurring of gray-level discontinuities in closed boundaries, and on the other hand the planned Texture variations do not cluster along geometry curves

The final result is that it cannot effectively represent the geometric texture direction in the image, and cannot fully describe the posture and feature information of the person in it, resulting in ambiguity and ambiguity for later motion tracking and recovery.

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