Quick detecting method for moving objects in dynamic scene

Abstract

Provided is a quick detecting method for moving objects in a dynamic scene. The quick detecting method for the moving objects in the dynamic scene comprises carrying out sequence interframe registration on moving images by utilizing CenSurE feature points and a homography transformation model, obtaining a registering frame of a former frame taking a current frame as reference, carrying out subtraction on the registering frame with the current frame to obtain a frame difference image to generate a foreground mask, building a dynamic background updated in real time according to space distribution information of the foreground mask in the current frame, obtaining a background subtraction image based on a background subtraction method, carrying out statistics on the probability density of the gray level of each pixel in the frame difference image, when the sum of the probability density of the gray level of a pixel is larger than 2phi(k)-1, taking the gray level as a self-adaptation threshold value, judging pixels with values of gray levels larger than the threshold value as foreground pixels, and otherwise judging the pixels as background pixels. The quick detecting method for the moving objects in the dynamic scene can reach the processing speed of 15frame / s and can obtain relatively integral moving objects under the premise that the detecting speed is ensured, and therefore, index requirements such as rapidity, noise immunity, illumination adaptation, target integrity and the like of the detection of the moving objects in the dynamic scene can be met.

Description

technical field [0001] The invention belongs to the technical field of civil aviation, in particular to a method for quickly detecting a moving target in a dynamic scene. Background technique [0002] Moving object detection is to extract moving objects from video sequence images, and it is an important basis for higher-level processing such as object recognition, tracking, and behavior analysis in computer vision. According to the motion state of the camera, moving object detection can be divided into two types: moving object detection in static scenes and moving object detection in dynamic scenes. Among them, the moving target detection technology in static scenes is relatively mature, and has been widely used in fixed video surveillance sites. Commonly used algorithms include background subtraction based on mixed Gaussian models. In dynamic scenes, since both the camera and the target are moving, it increases the difficulty of target detection, so it is a hot and difficu...

AI Technical Summary

Problems solved by technology

Among them, the fixed threshold binary method is suitable for the segmentation of foreground and background in static scenes. to valid foreground pixels

The OTSU method can determine the segmentation threshold according to the principle of maximum inter-class variance, and can adapt to changes in the scene. However, for the preliminary detection result map of the target with no obvious peaks and valleys in the gray histogram, the binary segmentation effect of the OTSU method is not good. , which will greatly increase the risk of target false detection in the scene

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