A real-time preprocessing method and storage medium for video surveillance data

Abstract

A real-time preprocessing method and storage medium for video surveillance data, the method comprising: obtaining the minimum channel map, and calculating the downsampled dark primary color D down , after converting the video image into a grayscale image, sort in descending order according to the grayscale value of the pixels, select a certain proportion of high-brightness pixels, and use the average value as the global atmospheric light estimation A; for the downsampled dark primary color D down Perform filtering processing to enhance the local small value to obtain the optimized down-sampled dark primary color D filter ; to D filter Up-sampling to get the scattered light estimation of each position of the video, according to the preset fog imaging model, using the scattered light estimation S and the global atmospheric light estimation A to inversely solve the fog-free picture of the t-th frame. The present invention obtains the dark primary color of down-sampling, reduces the amount of calculation, improves the running time, and is filter Scattered light can be estimated by up-sampling in blocks, and fog-free images can be reversed by blocks, which reduces the demand for memory space and is more suitable for the situation of uneven equipment in the Internet of Things environment.

Description

technical field [0001] The present application relates to a video data processing method, in particular, to a real-time preprocessing method for video surveillance data such as the Internet of Things. Background technique [0002] The Internet of Things monitoring is a commonly used means of security prevention. It uses the sensor network to react to the intuitive, accurate and timely information content of the video. However, current monitoring systems are very sensitive to weather conditions. In particular, under foggy conditions with low visibility, the observation target is blurred and the color fidelity drops significantly. Therefore, in order to improve the adaptability of the IoT monitoring system to harsh climates, it is necessary to preprocess the IoT fog video to achieve the purpose of improving the visual effect. [0003] At present, according to whether the fog imaging model is used, the existing video defogging methods can be roughly divided into two categorie...

AI Technical Summary

Problems solved by technology

However, the median filter does not have the feature of preserving edges, and cannot completely remove the block effect, and there will be halos after the video is defogged.

Although guided filtering and fast bilateral filtering have good edge-preserving characteristics and fast running speed, they use space for time, and the space consumption is relatively large, which is difficult to meet the environment where the hardware configuration of IoT devices is uneven.

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