Video image processing method suitable for video monitoring equipment

Abstract

The invention discloses a video image processing method suitable for video monitoring equipment. The method comprises the following steps: acquiring an image frame sequence; splitting the image frame sequence into a training set and a test set, and training the neural network model through the training set to obtain a trained neural network; extracting a foreground image, extracting a foreground image of each image frame, sequentially generating a fuzzy sequence of the foreground image of each image frame, sequentially judging whether each image frame has a fuzzy region or not according to the corresponding fuzzy sequence, if so, deleting the image frame with the fuzzy region, and if not, storing the image frame without the fuzzy region; according to the technical scheme, the volume of the image sequence formed by the video or the multiple images can be compressed, so that on the video monitoring equipment, the storage space required by a server side can be greatly compressed, the proportion of effective data can be increased, invalid fuzzy data can be cleared away, and the user experience can be improved.

Description

technical field [0001] The invention belongs to the technical field of image processing and mobile video processing, and in particular relates to a video image processing method suitable for video monitoring equipment. Background technique [0002] When video surveillance equipment collects videos of high-speed moving targets, most of the acquired images are very blurred, or are images in dimly lit environments, or exposed images under strong light. In the long-term continuous video data After collection, a large amount of these invalid data have been accumulated, and the storage space consumed by the video surveillance equipment on the server side is massive. If these relatively blurred images can be removed, the volume of the video or the image sequence composed of multiple images can be compressed , so that on the video surveillance equipment, the storage space required by the server can be greatly compressed, the proportion of valid data can be increased, invalid fuzzy d...

AI Technical Summary

Problems solved by technology

[0002] When video surveillance equipment collects videos of high-speed moving targets, most of the acquired images are very blurred, or are images in dimly lit environments, or exposed images under strong light. In the long-term continuous video data After collection, a large amount of these invalid data have been accumulated, and the storage space consumed by the video surveillance equipment on the server side is massive. If these relatively blurred images can be removed, the volume of the video or the image sequence composed of multiple images can be compressed , so that on the video surveillance equipment, the storage space required by the server can be greatly compressed, the proportion of valid data can be increased, invalid fuzzy data can be cleaned up, and the user experience can be improved. However, existing image processing methods do not There is no similar technology to solve the above problems. It is very time-consuming and labor-intensive to manually clean up unnecessary images or edit unrecognizable images of video frames.

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