Complex environment target detection method based on image processing

Abstract

The invention discloses a complex environment target detection method based on image processing. The method comprises the following steps: S1, obtaining a background image through a video, carrying out differential operation on a current image and the background image, and obtaining a gray scale histogram distribution map of the differentiated image; S2, through analysis of the gray scale histogram distribution map, calculating the asymmetrical coefficient of the gray scale histogram distribution map to determine whether a detection target exists, and calculating a separation coefficient for using the asymmetrical coefficient to determine whether the target appears; S3, carrying out environment matching if the target exists, and then using the asymmetrical coefficient of the differentiated image to determine a threshold for separating the background and the target; and S4, after determining the binary threshold, through marking differentiated image pixels, gray values of which exceed the threshold, and using the pixels as target pixels to detect the target. The method can differentiate background pixels from target pixels accurately in a complex and changing monitoring environment.

Description

technical field [0001] The invention relates to a detection method, in particular to a target detection method based on image processing in a complex environment. Background technique [0002] In the video detection system of modern expressways, a large number of image processing technologies are used to identify vehicle targets, but the current processing methods are poor in target recognition accuracy in complex environments and cannot meet actual needs. The Otsu method proposed by Japanese scholar Nobuyuki Otsu in 1979 is an adaptive threshold determination method, also known as the maximum inter-class variance method. According to the grayscale characteristics of the image, the maximum value of the inter-class variance of the pixel value between the background and the vehicle target is obtained, and the image is divided into two parts: the background and the target. The Otsu method requires good separability of the data itself, and because the inter-class variance must ...

AI Technical Summary

Problems solved by technology

This method is based on the existence of multimodal distribution in the histogram distribution of the difference image. This algorithm is feasible when there is a target, but in the surveillance image, not every frame of image will have a target, so if using This method will cause some false targets to be detected, and false positives often occur; at the same time, there is still a problem in this method, how to choose the proportion corresponding to the peak value in the histogram, if 10% is used, for complex and changeable monitoring environment, it is obviously inappropriate

Images