A shadow detection and elimination method for complex scene video

A shadow detection and complex scene technology, applied in the field of video processing, can solve the problems of eliminating large scenes, difficult batch processing, and difficulty in labeling complex scene shadow images, and achieve the effect of reducing interference

A shadow detection and complex scene technology, applied in the field of video processing, can solve the problems of eliminating large scenes, difficult batch processing, and difficulty in labeling complex scene shadow images, and achieve the effect of reducing interference

CN110349099BActive Publication Date: 2021-04-02WUHAN UNIV
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  • A shadow detection and elimination method for complex scene video
  • A shadow detection and elimination method for complex scene video
  • A shadow detection and elimination method for complex scene video

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Embodiment Construction

[0045] The present invention will be further described in detail below in conjunction with examples and drawings, but the embodiments of the present invention are not limited thereto.

[0046] refer to figure 1 , flow chart of the present invention, a kind of video shadow elimination method, comprises the following steps:

[0047] Step 1. For the input video stream V, its depth information needs to be obtained first: for the video shot on the spot, use Kinect V2 to collect the depth information of the video in real time; for the existing video, use the deep learning method to estimate the depth information of each frame of the video. depth information. Such as image 3 (a) and (b) show the input video frame and the corresponding depth map in the example, respectively.

[0048] Step 2, use the texture filter operator to filter each frame I in the video to reduce the impact of small-scale texture on shadow detection, while retaining the original shadow information.

[0049] ...

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Abstract

The invention discloses a video shadow detection and elimination method based on depth information. First, the depth information of the image is used to estimate the normal information and point cloud position information of each pixel point. By comparing each pixel point and its spatio-temporal local part in the video stream, The feature similarity between neighborhood pixels is used to estimate the shadow confidence value of each pixel, and the Laplacian operator is used to further optimize the shadow confidence to obtain the final shadow detection result. Finally, the shadow detection result is used to construct a The illumination recovery optimization equation of the video flow is used to obtain the final shadow elimination result. The present invention has the following advantages: it uses texture filtering to effectively reduce the interference of texture information on shadow detection, uses the Laplacian operator to optimize the initial shadow confidence, and obtains more complete shadow detection results, and uses chromaticity constraints and previous and subsequent frames. Correlation eliminates shadows, which can effectively ensure the chroma invariance and inter-frame continuity of the results.

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...

Claims

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Application Information

Patent Timeline
02 Apr 2021
Publication
CN110349099B
IPC
G06T5/00
CPC
G06T2207/10016; G06T5/94; G06T5/70
Inventors
肖春霞; 吴文君