Video segmentation method based on strong target constraint video saliency

Abstract

The invention, which belongs to the technical field of image processing, discloses a video segmentation method based on strong target constraint video saliency. According to the method disclosed by the invention, strong target constraint is introduced based on image saliency. The location and scale constraint of a target are obtained by a multi-scale tracking algorithm and optical flow correction,color constraint information of the target is obtained based on a historical frame segmentation result, and calculation is carried out obtain a video saliency result; histogram classification is carried out on the video saliency result to obtain a tag mask graph, and foreground / background prior probability models of a current frame are calculated; a super-pixel-based time-space continuum full connection condition random field model is constructed at the current frame, data items are defined by using the prior probability models, an intra-frame smooth item and an inter-frame smooth item are defined by combining color distances, space distances and edge relationships between super pixels, and optimized solution is carried out by using a fast high-dimensional Gaussian filter algorithm to complete video target segmentation. Therefore, the accuracy and the time efficiency of video segmentation are improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more particularly relates to a video segmentation method based on strong target constrained video saliency. Background technique [0002] Video segmentation is a technique to segment out the foreground object in each frame of image in the video, that is, to completely determine the foreground contour. Video segmentation is often at the bottom of the video processing algorithm, and its segmentation results are used for higher-level video application problems, such as video foreground feature extraction, foreground classification, and foreground recognition. At the same time, in industrial production, there is also a demand for video segmentation, such as video editing, 3D video reconstruction, and video semantic analysis. [0003] In the video segmentation problem, it is necessary to segment the foreground area in each frame of the image. Generally, the video segmentation algorithm ...

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Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a video segmentation method based on strong object constrained video saliency, the purpose of which is to use strong object constrained video saliency extraction and superpixel-based spatio-temporal continuous full connection condition Random field video segmentation algorithm, thus solving the problem of inaccurate and inefficient existing video segmentation technology

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