Video data real-time processing method and device based on adaptive tracking frame segmentation

Abstract

The invention discloses a video data real-time processing method and device based on adaptive tracking box segmentation, a computer device and a computer storage medium. The method comprises the stepsof obtaining a (t)th frame image comprising a specific object in a group of frame images, and a tracking box corresponding to a (t-1)th frame image; adjusting the tracking box corresponding to the(t-1)th frame image according to the (t)th frame image, thereby obtaining the tracking box corresponding to the (t)th frame image; carrying out scene segmentation on a partial area of the (t)th frame image according to the tracking box corresponding to the (t)th frame image, thereby obtaining a segmentation result corresponding to the (t)th frame image; determining a second foreground image of the (t)th frame image according to the segmentation result; adding a personalized special effect according to the second foreground image, thereby obtaining a processed (t)th frame image; covering the (t)thframe image through utilization of the processed (t)th frame image, thereby obtaining processed video data; and displaying the processed video data. According to the technical scheme, the personalized special effect can be added to the frame image relatively precisely and rapidly.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a video data real-time processing method, device, computing device and computer storage medium based on adaptive tracking frame segmentation. Background technique [0002] In the prior art, when the user needs to perform personalized processing such as changing the background of the video, adding special effects, etc., the image segmentation method is often used to perform scene segmentation processing on the frame images in the video. Among them, the image segmentation method based on deep learning is adopted The segmentation effect at the pixel level can be achieved. However, when the existing image segmentation method performs scene segmentation processing, it is necessary to perform scene segmentation processing on all the contents of the frame image, the amount of data processing is large, and the processing efficiency is low; in addition, the existing image...

AI Technical Summary

Problems solved by technology

However, when the existing image segmentation method performs scene segmentation processing, it is necessary to perform scene segmentation processing on all the contents of the frame image, the amount of data processing is large, and the processing efficiency is low; in addition, the existing image segmentation method is performing scene segmentation When processing, the proportion of the foreground image in the frame image is not considered, so when the proportion of the foreground image in the frame image is small, it is easy to divide the pixels that actually belong to the edge of the foreground image by using the existing image segmentation method is the background image, the resulting segmentation results have low segmentation accuracy and poor segmentation effect

Therefore, the image segmentation method in the prior art has the problems of a large amount of data processing for image scene segmentation, low processing efficiency and low segmentation accuracy, so the obtained segmentation results cannot be used to accurately and accurately classify the frames in the video. Personalized special effects are added to the image, and the resulting processed video data has a poor display effect

Images