Method and device for detecting moving object

Abstract

The embodiment of the invention discloses a method and device for detecting a moving object, wherein the method and device are used for eliminating influences of sudden scene changes and rapid and gradual scene changes on detection of the moving object, decreasing the false detection rate and reducing operation cost. The method includes the steps of detecting a current scene, determining that the current scene is a stable scene or a variable scene, determining the type of the variable scene after the current scene is determined to be the variable scene, comparing the difference value between the gradient model vector similarity of a current frame and the gradient model vector similarity of an Nth frame with a preset threshold value if the variable scene is determined to be a rapidly and gradually changed scene, determining the type of the variable scene again, and renewing background modeling parameters of a Gaussian mixture model after the type of the variable scene is determined again and when the variable scene is determined to be a suddenly changed scene or a rapidly and gradually changed scene, wherein the variable scene can be the suddenly changed scene, the rapidly and gradually changed scene and a slowly and gradually changed scene, and N is a positive integer.

Description

technical field [0001] The present invention relates to the field of communication technology, in particular to a method and device for detecting a moving object. Background technique [0002] Mixed Gaussian background modeling is one of the most commonly used moving target detection methods at present. This method assumes that under the influence of noise, the color value change of each pixel in the background image conforms to the Gaussian model. By establishing multiple corresponding Gaussian models for each pixel in the video image frame, a complete background model is formed, and the parameters (mean, variance and weight) of each Gaussian model are learned online. [0003] The moving object detection method judges whether a pixel is a background pixel or a foreground object according to the mean value and variance information of the Gaussian model. If the feature of the pixel in the current frame matches the background Gaussian model of the corresponding pixel, the pix...

AI Technical Summary

Problems solved by technology

However, the mixed Gaussian background model is updated slowly, so it is difficult to adapt to sudden disturbances or scene jitters. If the update is too fast, it will lead to a large number of false detections; moreover, the Gaussian model has high computational overhead. model to enhance adaptability, which makes the overhead higher

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