Background modeling method based on pixel confidence

Abstract

The invention discloses a background modeling method based on pixel confidence, which comprises judging whether a pixel point is a background according to the pixel confidence, that is, the possibility that the pixel point is the background, specifically comprising the following steps of: firstly reading video frames in order, judging the size of an HSV pixel value at a corresponding point of twoadjacent frame images so as to judge whether the point is stable, and if so, adding a continuous stable count value; otherwise, zeroing the continuous stable count value at the pixel point; then performing the non-linear normalization process on the continuous stable count values of all pixel points on the image to obtain unmodified confidence at each pixel point on the current image; then clustering background candidate list colors at each pixel point in the image and updating the cluster confidence; and finally taking a pixel value with the highest cluster confidence as a current background, continuously reading the next frame image. The background can be accurately extracted to get ready for obtaining a monitored object; and the background modeling method can be widely applied to the video monitoring fields such as object tracking, movement analysis and the like.

Description

technical field [0001] The invention relates to the technical field of video tracking image segmentation, in particular to a background modeling method based on pixel confidence. Background technique [0002] As the society pays more and more attention to safety, video tracking technology has become a hot topic and has been widely used in video surveillance, automatic traffic monitoring, human body detection and tracking and other fields. Generally speaking, a typical video tracking system uses a static camera to monitor a fixed area in real time, and then segments the foreground target from the captured scene background and classifies and tracks it. From the perspective of whether people pay attention, video image information can be divided into foreground target and background. If the algorithm can accurately model the background and eliminate the uninteresting complex background in the video image, the attention of the subsequent processing process can be focused on Fore...

AI Technical Summary

Problems solved by technology

However, the above algorithm cannot achieve satisfactory results when the foreground object moves chaotically and the background is disturbed, and its real-time and accuracy cannot meet the requirements of the application.

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