Accurate moving shadow detection method based on multi-feature fusion

Abstract

The invention relates to an accurate moving shadow detection method based on multi-feature fusion, belonging to the field of video image processing. Firstly, the foreground image in a video is extracted, and six features of brightness, color and texture of the foreground image are extracted. In order to describe the features as roundly as possible, under the brightness constraint, the color information of multiple color spaces and multi-scale images are extracted. Meanwhile, the texture information is respectively described through entropy and a local binary pattern. Secondly, the features are fused to generate a feature pattern. Then, a moving shadow can be roughly determined on the feature pattern. Finally, in order to obtain an accurate shadow detection result, misclassified pixels are corrected through space adjustment. A lot of experiment and comparison results express that the method has good performance and is superior to the current shadow detection method.

Description

technical field [0001] The invention belongs to the field of video image processing. Background technique [0002] In many computer vision applications, such as object tracking, object recognition, video surveillance, video compression, etc., moving object detection is a basic and important task. Background subtraction is a common method for detecting moving objects. However, shadows always move with their corresponding objects so that many background subtraction methods cannot separate them accurately. Inaccurate judgments may result in target merging, target shape distortion, or even target loss. Therefore, detecting and eliminating shadow regions is a very critical and important research problem in the field of video processing and motion analysis. [0003] Generally speaking, the existing shadow detection methods can be divided into four categories according to different characteristics: chrominance-based, physical model-based, geometry-based and texture-based methods...

AI Technical Summary

Problems solved by technology

Although many existing methods utilize the fusion of different features for shadow detection, different metrics for the same type of features have not been fully considered.

In addition, most of these methods detect shadow pixels in a serial mode rather than in parallel, so that the mutual complementarity between multiple features is not fully displayed.

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