A Foreground Detection Method Based on Adaptive Background Update and Selective Background Update

Abstract

The invention discloses a foreground detection method based on adaptive background updating and selective background updating, which belongs to the technical field of image processing. The present invention first converts the image frame of the video stream to be detected into a grayscale image and an HLS image; if the current image frame is the first frame of the video stream, its grayscale image is used as the background model; for the image frame other than the first frame, If the number of image frames does not exceed the threshold, if so, then continuously update the background model based on the grayscale image of the current frame until the number of image frames exceeds the threshold; when it exceeds the threshold, enter the foreground image detection processing step: based on the background model and the current The grayscale image of the frame is used to obtain the first foreground image, and then the frame difference method is used to obtain the second foreground image, and the union of the two is obtained to obtain the third foreground image, and the third foreground image is processed through the color normalized correlation coefficient, so that Get pixel-level foreground objects. The invention has good detection effect on moving and still objects, and has better robustness.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to the field of foreground target detection. Background technique [0002] Foreground detection, as an important part of intelligent video surveillance, has received extensive attention in recent years. The performance of foreground detection is critical for subsequent processing such as object classification, object tracking, and behavior understanding. It has become one of the key research directions of research institutions. In recent years, many effective methods have been proposed. Although the detection speed is fast and the background modeling is simple, the target residual phenomenon is serious, especially for slow moving and stationary targets. The detection effect is not satisfactory. [0003] For example, Barnich O, Van Droogenbroeck M proposed a ViBe method called visual background extraction in "ViBe-a powerful technique for background detection and subt...

AI Technical Summary

Problems solved by technology

However, since the algorithm itself considers the ablation of the Ghost area in the background, it is completely unable to effectively detect the slow moving speed and stationary targets.

Images