Moving object detection method capable of automatically adapting to complex scenes

Abstract

The invention discloses a moving object detection method capable of automatically adapting to complex scenes, which comprises the steps of 1) carrying out illumination compensation on a video image; 2) acquiring a background image of each frame of the video image by using a mixed Gaussian background modeling method; 3) acquiring an absolute difference image of each frame by using a background difference method principle; 4) acquiring an optimal segmentation threshold of a gray probability model of each absolute difference image by adopting a maximum entropy segmentation principle; 5) carrying out binarization processing on each absolute difference image by using the optimal segmentation threshold so as to acquire a foreground image; 6) carrying out morphological processing by adopting modules with different structures; and 7) carrying out region calibration on each foreground image by using a connected domain calibration algorithm, and locking a calibrated moving object by using a rectangular frame. The moving object detection method disclosed by the invention has good moving object adaptive detection accuracy and robustness in different complex scenes such as drastic changes in global illumination, background disturbance, relative movement and the like, and can improve the performance of object detection.

Description

technical field [0001] The invention relates to video intelligent monitoring technology, in particular to an adaptive complex scene moving target detection method, which belongs to the technical field of image processing. Background technique [0002] Moving object detection technology is one of the key technologies in the field of video intelligent surveillance technology, and it is the basis for follow-up research such as object identification, tracking, and behavior analysis. Commonly used moving target detection techniques include optical flow method, frame difference method, and background difference method. Among them, the optical flow method is a method of estimating the movement of pixels in a sequence of images between consecutive frames. Since this method only cares about the pixels of the image and does not associate pixels with moving objects, it is difficult for objects with irregular contours. Accurate positioning is achieved, and the calculation is complex. ...

AI Technical Summary

Problems solved by technology

Using traditional Gaussian background modeling, average background modeling, median background modeling, etc., are susceptible to factors such as weather changes, sudden changes in illumination, background disturbances, and relative motion between the camera and the target, and the fixed threshold is not adaptable, for example, the threshold If the selection is too low, it is not enough to suppress the noise in the image; if the selection is too high, the useful changes in the image will be ignored; for a large moving target with consistent color, there may be holes inside the target, and the moving target cannot be completely extracted. question

Although the background subtraction method has a better target detection effect in the case of a static background and an ideal scene, due to complex actual scenes, weather changes, sudden changes in global illumination, background disturbances, and relative motion between the camera and the target, it is easy to cause moving target detection. Inaccurate

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