Method for tracing human body movement based on maximum geometric flow histogram

Abstract

The present invention discloses a method for tracing human body movement based on a maximum geometric flow histogram, and mainly solves defaults such as fuzziness of human contour and edge descriptions, and failure to reflect characteristic internal geometric structure and texture mode in existing characteristic extraction method. Realization processes are: inputting video images to be treated and extracting a block diagram of a major human body part; performing a two-dimensional multi-scale wavelet transform to the image; searching for an optimal geometric flow direction by using quadtree division and bottom-up fusion principles; performing a one-dimensional wavelet transform to the quantized optimal geometric flow direction signals which are then reconstructed to a two-dimensional form and obtain a coefficient matrix; counting geometric flow coefficient strength histogram of 9 directions of each area as a final image characteristic expression; and through a regression progress, learning mapping relations from image characteristics to three-dimensional movement data, and predicting and recovering three-dimensional postures of the new training video images. The method for tracing human body movement based on the maximum geometric flow histogram, which is of fast computing speed and accurate results, reinforces image characteristic robustness, and can be applied to human body target identification, detection, and posture reconstruction.

Description

technical field [0001] The invention belongs to the technical field of video image processing, and mainly relates to a video image feature texture representation method, specifically a human body motion tracking method based on the second-generation strip wave maximum geometric flow direction histogram, 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 recent decades. People are the core content and reflect the core semantic features of images. Related technologies have been initially applied in many fields such as motion capture, human-computer interaction, and video surveillance, and have 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, machine learnin...

AI Technical Summary

Problems solved by technology

However, most of the image feature representation methods describing the human body are based on contour and edge information, which is not strict in theory. For a single static image, it can express the general content of the image to a certain extent, but for continuous image sequences with slight changes , it is difficult to better describe the internal information of the image

At the same time, there is also a major problem in such edge-based methods. The local rapid change of the image often cannot correspond to the discontinuity jump along the edge curve. The resulting texture variations do not cluster along geometric curves

The end result is that it cannot accurately and 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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