Motion detection and error rate reduction method based on block matching

Abstract

The invention discloses a motion detection and error rate reduction method based on block matching. The motion detection and error rate reduction method based on the block matching mainly includes following steps: obtaining an image order which comprises frame images at a t-1 moment and a t moment; performing block matching arithmetic on the frame images at the t-1 moment and the t moment, and finding out a motion vector of each block; performing outlier detection on the vectors from the t-1 moment to the t moment and revising outliers; clustering vectors in a revised image. The motion detection and error rate reduction method based on the block matching can effectively achieve good effects in video with a moving background, especially avoids the special circumstance that the moving distance of a foreground in a frame is less than the moving distance of the background when a camera moves along with the moving foreground, can effectively adapt to different shooting modes, and avoids a mismatching problem caused by an occlusion problem between each two adjacent frames.

Description

Technical field: [0001] The invention belongs to the technical field of image processing and computer vision, and in particular relates to a motion detection based on block matching and a method for reducing error rate. Background technique: [0002] Moving target detection is the basis and key point of intelligent analysis and processing in the smart TV video system. Its purpose is to detect the changing area in the sequence image and extract the moving target from the background image for the follow-up It provides support for algorithms such as moving target recognition, tracking, behavior analysis, and depth assignment. [0003] At present, the commonly used moving target detection algorithms mainly include frame difference method, optical flow method and background difference method. Among them, the inter-frame difference method is simple to calculate and has strong real-time performance, but its calculation error is relatively large, and it cannot handle the situation ...

AI Technical Summary

Problems solved by technology

However, the detection accuracy of the background difference method depends on the accuracy of the background image construction, that is, when the background reference image is constructed, it needs to be able to truly reflect the current scene, and whether the real-time update of the parameters can reflect the changes of the background. These background changes include: changes in illumination (including Slow lighting changes and sudden changes in lighting, etc.), moving objects such as leaves, clouds, smoke, rain, and fog in the background that swing with the wind, as well as camera shake and shadow problems, etc., and background subtraction cannot handle background motion.

Since these difficult factors need to be considered in the construction of background reference images, it is currently difficult to construct a good background modeling and adaptive background image update algorithm.

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