Method for modeling background based on camera response function in automatic gain scene

Abstract

The invention discloses a method for modeling a background based on a camera response function in an automatic gain scene, which comprises the following steps of: performing automatic gain progressiveness-based analysis to obtain a roughly divided background area, obtaining low-noise training data by using a joint histogram method, and performing recovery once to obtain a globally optimal camera response function by the method based on maximum likelihood estimation and parameter constraints; online calculating a gain ratio frame by frame by utilizing correlation between a foreground and background difference and the gain ratio and the homography of a grayscale difference function relative to the gain ratio; and if the gain ratio is not 1, performing updating to obtain a background reference frame the same as a current reference frame by using the camera response function and the gain ratio, otherwise determining the background reference frame is unchanged, and obtaining the backgroundreference frame with a gain coefficient the same as that of the current frame along with the change of the gain coefficient of a camera. By the method, the shortcomings of high background change speed, caused by difficulties in realizing automatic gain along with the camera, of the conventional methods are overcome, thereby ensuring high-efficiency motion detection.

Description

technical field [0001] The invention relates to the fields of image processing and computer vision, in particular to a background modeling method for accurately following changes in gain coefficients in camera automatic gain scenes to obtain precise motion detection. Background technique [0002] Motion detection is an important research direction of computer vision, and it is also a key and basic module in many computer vision applications, such as video semantic annotation, pattern recognition, traffic video surveillance, and human body tracking. The purpose of motion detection is to completely segment the moving object of interest from the video. Whether the segmentation is accurate or not directly affects the accuracy of subsequent modules. [0003] Motion detection methods can be classified into the following categories [1] : Optical flow method, frame difference method, background difference method. In the fixed camera scene, the background subtraction method has be...

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Problems solved by technology

[0012] The problem to be solved by the present invention is: in the motion detection of the background difference method, the existing background modeling method cannot judge whether the rapid change of the gray value of the large-scale pixel is the foreground or the background after automatic gain, there is a large error, and training data needs to be collected in advance and other defects, which are not conducive to the fully automatic realization of background difference method motion detection, and are easily affected by noise, etc.

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