Complex scene video shadow detection and elimination method

Abstract

The invention discloses a view screen shadow detection and elimination method based on depth information. The method comprises the following steps: firstly, estimating normal information and point cloud position information of each pixel point by utilizing depth information of an image; comparing the feature similarity between each pixel point and a spatio-temporal local neighborhood pixel point in a video stream of each pixel point to estimate the shadow confidence value of each pixel point, further optimizing the shadow confidence by using a Laplace operator to obtain a final shadow detection result, and finally constructing an illumination recovery optimization equation based on a video stream by using the shadow detection result to obtain a final shadow elimination result. The method has the advantages that the interference of texture information on shadow detection is effectively reduced through texture filtering, the initial shadow confidence coefficient is optimized through a Laplace operator, a more perfect shadow detection result is obtained, shadows are eliminated through chrominance constraints and correlation between a front frame and a rear frame, and chrominance invariance and inter-frame continuity of the result can be effectively guaranteed.

Description

technical field [0001] The invention belongs to the technical field of video processing, in particular to a method for detecting and eliminating video shadows in complex scenes. Background technique [0002] Shadows are common natural phenomena in our daily life. They can provide important information for the understanding of visual scenes, such as lighting environment, scene geometry and other information. This information plays an important role in applications such as lighting analysis, relighting, and augmented reality. Therefore, effectively detecting and removing shadows is an important topic in the field of computer vision. However, automatic detection and removal of shadows is a very difficult task, which is not only affected by local texture and material information, but also needs to consider the global structure information and lighting environment information in the scene. Most of the existing shadow detection and elimination algorithms are based on local chrom...

AI Technical Summary

Problems solved by technology

There are two main reasons why it is difficult to eliminate shadows in complex scenes. First, the material texture information in complex scenes is relatively rich, and the distribution of shadows is also messy and not concentrated, which increases the difficulty of detection. Even with the prior knowledge of human interaction, complex The shadow scene will increase the burden of labeling, and it is difficult to perform efficient batch processing; secondly, due to the difficulty of labeling and the lack of corresponding data sets, it is difficult to use deep learning methods to eliminate shadows in large scenes.

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