Visual characteristic defogging and image stabilizing detection system

Abstract

The invention relates to a visual characteristic defogging and image stabilizing detection system including the following modules and functions. First, image data is input into a fog feature analysisand enhancing module and an enhanced image and a correction parameter 1 are acquired through calculation. Second, the enhanced image is input to a multi-level parameter feedback control based object detection system module which calculates out the position of a to-be-detected object through detection and recognition and outputs a target displacement and a correction parameter 2. Third, an image stabilizing displacement calculating module outputs image stabilizing displacement to an image stabilizing flyback integrated control platform. Fourth, the image stabilizing flyback integrated control platform implements an image stabilizing function according to the image stabilizing displacement and adjusts sensor integral time according to the correction parameter 1 and the correction parameter 2. According to the invention, the system implements functions of visual characteristic analysis based defogging enhancement, photoelectric mechanical integrated image stabilization, detection, tracking and the like, achieves good definition, good stability and good accuracy and can be embedded into target detection systems of different types.

Description

technical field [0001] The invention relates to a visual characteristic defogging and image stabilization detection system, which belongs to the interdisciplinary field of automatic control and electronic information. Background technique [0002] Target tracking refers to the task of giving the position of the target in a certain frame of the video, and then the algorithm continuously calculates the target position in the subsequent frames. At present, one of the mainstream target tracking algorithms is based on the target detection method. The commonly used detection methods include inter-frame difference, background difference and motion segmentation. The inter-frame difference uses the change between frames to realize the detection, and the common method is two-frame or three-frame difference method. The background difference method uses the background image or reconstructs the background through the model, and obtains the moving target area by difference with the curre...

AI Technical Summary

Problems solved by technology

For the inter-frame difference method, when the displacement of the moving target is small, the inter-frame difference cannot segment the target

The inter-frame difference method cannot deal with the problem of simultaneous motion of the background, and cannot isolate the background from the target

The background difference method cannot deal with the background motion problem. When the background continues to move, the background difference method cannot successfully construct the background model, making the difference impossible.

The defect of the motion segmentation method is that the calculation of optical flow will contain more noise, and it is difficult to accurately distinguish the noise from the target when performing optical flow segmentation, resulting in a high false alarm rate of the algorithm

None of these methods have made special processing for the light transmission characteristics of haze, and the processed image has more or less distortion, which affects further enhancement processing.

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