Template matching monitoring video defogging system and its defogging method based on cloud platform

Abstract

The invention discloses a cloud platform-based template matching monitoring video defogging system and its defogging method. It combines the adaptive clustering K-means algorithm and the dark primary color prior algorithm to perform defogging and reduce terminal equipment performance requirements and power consumption. . The beneficial effects achieved by the present invention are: when dehazing key frames, the key frames are divided based on the fog concentration and dehazed independently, which weakens the influence of the outline, shape and size of background objects, and weakens the differences in the dehazing process. The mutual influence between adjacent scenes in depth; when dehazing non-key frames, we make full use of the invariance of the viewing angle and the spatiotemporal correlation of each frame, and greatly speed up the calculation by training the transmittance template library unique to each terminal device. This makes it very effective for defogging fog-containing videos acquired from fixed viewing angles; it reduces the power loss and performance requirements of terminal equipment, and provides a good platform for achieving better defogging effects and processing larger amounts of video data.

Description

Technical field [0001] The invention relates to a template matching monitoring video defogging system and a defogging method based on a cloud platform, and belongs to the technical field of video processing. Background technique [0002] With the development of image processing and computer vision technology, the demand for outdoor vision systems represented by road traffic video surveillance systems has increased; haze weather has largely limited the role of video surveillance systems; current video defogging systems are rare Considering the inter-frame correlation and its image structure information, the process of processing has a strong dependence on the collection of weather and scene information, and is complex to the environment (such as occlusion, excessive gap in the depth of the scene in the video, and the sky area in the video) The defogging of the video is not good; the defogging operation of the video puts forward higher requirements on the performance of the device,...

AI Technical Summary

Problems solved by technology

[0002] With the development of image processing and computer vision technology, the demand for outdoor vision systems represented by road traffic video surveillance systems has increased; fog and haze weather has largely limited the role of video surveillance systems; there are few current video defogging systems Considering its inter-frame correlation and its image structure information, the processing process has a strong dependence on the collection of weather and scene information, and the environment is complex (such as occlusion, too large depth gap of the scene in the video, and the sky area in the video) The defogging operation of the video is not good; the defogging operation of the video puts forward higher requirements on the performance of the equipment, and it is necessary to find an effective video defogging method and system

[0003] At present, the research on video defogging methods has achieved many meaningful results. For example, the Langley Research Center in the United States has conducted in-depth research on the domain-based Retinex algorithm to enhance foggy videos. However, it does not take into account the temporal and spatial correlation of video image sequences and frames. Inner image structure features, there are redundant processing and dependence on the scene environment, and greatly consume the performance and power of the terminal device

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