Group sparsity robust PCA-based moving object detecting method

Abstract

The invention discloses a group sparsity robust PCA-based moving object detecting method and belongs to the technical field of graphic information processing. The method comprises the steps of inputting a video sequence; conducting region segmentation with the over-segmentation algorithm to generate multiple isotropical regions serving as grouping information of group sparsity constraint; setting relevant parameters, and conducting iteration solving with the augmented Lagrangian multiplier method; estimating a moving object matrix through group sparsity constraint; estimating a background matrix through nuclear norm constraint, updating a multiplier and a penalty parameter; judging convergence, outputting an obtained background and an obtained moving object if convergence is realized, and continuing to conduct iteration if not. According to the method, a group sparsity robust PCA moving target detection model is established by means of movement distribution continuity prior, whether each isotropical region is the moving target is judged through the group sparsity norm, and in this way, the region boundary of the moving target can be measured more accurately, the robustness of complicated background movement is improved, and robust detection of the moving target is realized.

Description

technical field [0001] The invention relates to a video moving target detection method based on Group Sparsity (Group Sparsity) robust PCA, belonging to the technical field of image information processing. Background technique [0002] Moving object detection is an important part of effective video analysis. Most of the traditional object detection is realized by background subtraction method or frame difference method. Background modeling in the background subtraction method is very important to the detection of moving objects. However, the traditional background modeling is computationally complex, and the segmentation accuracy is easily affected by noise, and is sensitive to environmental changes such as illumination changes and dynamic textures. The inter-frame difference method obtains the outline of the moving object by performing difference operations between two adjacent frames. The algorithm is simple to implement, but it cannot extract the complete area of ​​the o...

AI Technical Summary

Problems solved by technology

However the classic l 1 The norm does not imply the "structural sparsity" that the coefficient itself is related to scale and structural information, and cannot effectively measure the correlation of this spatio-temporal context. The separated objects have problems such as incompleteness and disconnection, and the motion cannot be effectively used. The priori of the spatial-temporal distribution continuity of the target is also not conducive to eliminating the unstructured sparse components caused by noise and background random disturbances. Therefore, it is necessary to construct a structured sparsity metric. While maintaining the sparsity constraints, more Focus on the measurement of the spatial-temporal correlation of the moving target area, and then robustly segment the moving target

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