Target tracking method and device based on video noise reduction, and computer equipment

Abstract

The invention discloses a target tracking method and device based on video denoising, computer equipment and a storage medium, and the method comprises the steps: carrying out the video denoising processing of a noise video according to a video denoising processing model, and outputting a denoised video; according to a target tracking model and the denoised video, tracking the target object to obtain tracking data of the target object; and processing the tracking data to obtain the processed tracking data, and generating the trajectory video of the target object based on the processed tracking data, therefore, by adopting the embodiment of the invention, the effective video noise reduction processing can be performed on the noise video through the video noise reduction processing model, and the video noise reduction processing efficiency is improved. According to the method, more image features can be reserved while image noise information is removed, so that the output denoised video has better definition, detection and tracking of the target object are facilitated, and the tracking accuracy of the target object is effectively improved.

Description

technical field [0001] The present invention relates to the technical field of video processing, in particular to a method, device and computer equipment for object tracking based on video noise reduction. Background technique [0002] Visual target tracking needs to detect, extract, identify and track the moving target in the video sequence, and obtain the motion parameters of the moving target, but the image noise in the video will cause the target area to be affected by the noise and be difficult to be retrieved, affecting the moving target. Acquisition of the motion parameters, resulting in poor tracking effect and affecting the subsequent processing and analysis of the moving target. [0003] In view of the above situation, it is necessary to perform noise reduction processing on the video before target detection and tracking. While maintaining the integrity of the original information as much as possible, it can also remove useless information in the video sequence, wh...

AI Technical Summary

Problems solved by technology

[0005] Traditional noise reduction algorithms such as mean filter, median filter and bilateral filter cannot remove the noise in the video sequence very well

The mean filter itself has inherent defects. It cannot protect the image details well, and it also destroys the details of the image while performing image noise reduction, thus making the image blurred.

Also in images with more details such as points, lines, and spires, median filtering will cause loss of image information

However, bilateral filtering cannot cleanly filter out high-frequency noise in color images, and can only filter low-frequency information better.

These shortcomings of the traditional noise reduction algorithm will affect the follow-up target tracking effect, and even make the target tracking effect worse

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