Figure motion change detecting method based on extracting function and three-channel separation

Abstract

The invention discloses a figure motion change detecting method based on an extracting function and three-channel separation, which mainly solves the problem that in the prior art, the figure motion change detecting method is limited and incomplete in outline. The realization process is as follows: firstly, creating a gray-scale map background and a YUV (Luma and Chroma) three-channel background of the first 100 frames of a figure motion video by an averaging method; using an extracting function method to process the gray-scale map and the gray-scale map background of a to-be-detected image to obtain the result of the extracting function method; performing YUV three-channel separation to the to-be-detected image to obtain three single-channel images; respectively obtaining absolute differences between the three images and the corresponding channels; and utilizing an Otsu threshold method to binarize the three absolute differences to obtain three single-channel result images, fusing the three single-channel result images into an image by a mechanism of selecting two from three, and performing OR operation to the image and the result of the extracting function method to obtain the final result. The method is simple in algorithm and complete in figure outline, and can be used for detecting the figure motion change in an actual monitor system.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to image change detection, in particular to an intelligent detection method for character movement changes based on extraction functions and three-channel separation, which can be used in many fields such as intelligent monitoring and intelligent assisted driving. Background technique [0002] Change detection is to obtain the required target change information according to the difference between images through the comparison and analysis of images in different periods. The purpose of detection and tracking of moving targets is to realize the positioning, recognition and tracking of targets in the scene through the analysis of video images, so as to analyze the target behavior and respond to abnormal situations. Moving target detection detects changes in the scene image in the monitored scene in real time and extracts new targets to prepare for further target recognition and a...

AI Technical Summary

Problems solved by technology

[0007] Although the optical flow method has great advantages in dealing with background motion and occlusion problems, it still has many shortcomings: (1) The basic optical flow constraint equation is not strictly established, only at points with large gradients or some special Surface structure, such as in the case of motion where diffuse reflection and translation are dominant, the basic optical flow constraint equation is strictly valid; (2) There are large noises and errors in the calculation of optical flow, the reason is that in addition to the basic optical flow constraint equation In addition to being not strictly established, the sensitivity of differential motion to noise and the imperfection of additional constraints make it difficult to accurately calculate optical flow from noisy images

(3) The amount of calculation of optical flow is too large, and iterative calculation is generally required, so it is time-consuming, and the higher the accuracy of the optical flow algorithm, the greater the calculation cost. Ordinary digital signal processing chips and hardware system architecture are incompetent , so the cost of the optical flow method is higher than that of other algorithms

However, the motion detection of people is generally used indoors, and the indoor light can be considered to have little change, so there is no need to consider the update of the background, but because the outline of the person is an irregular arc, the simple background subtraction method is not very good Detect the outline of the human body

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