Shadow detection based on texture feature in gray sequence images

HAN Yan-xiang; ZHANG Zhi-sheng; HAO Fei; CHEN Ping

doi:10.3788/OPE.20132111.2931

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Shadow detection based on texture feature in gray sequence images

更新时间：2020-08-12

- Shadow detection based on texture feature in gray sequence images
- Optics and Precision Engineering Vol. 21, Issue 11, Pages: 2931-2942(2013)
- 作者机构：
  
  东南大学机械工程学院，江苏南京 211189
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132111.2931
  CLC： TP391.4
- Received：14 January 2013，
  
  Revised：28 March 2013，
  
  Published Online：22 November 2013，
  
  Published：15 November 2013
- 稿件说明：
移动端阅览
韩延祥, 张志胜, 郝飞, 陈平. 灰度序列图像中基于纹理特征的移动阴影检测[J]. 光学精密工程, 2013,21(11): 2931-2942

HAN Yan-Xiang, ZHANG Zhi-Qing, HAO Fei, CHEN Beng. Shadow detection based on texture feature in gray sequence images[J]. Editorial Office of Optics and Precision Engineering, 2013,21(11): 2931-2942
韩延祥, 张志胜, 郝飞, 陈平. 灰度序列图像中基于纹理特征的移动阴影检测[J]. 光学精密工程, 2013,21(11): 2931-2942 DOI： 10.3788/OPE.20132111.2931.

HAN Yan-Xiang, ZHANG Zhi-Qing, HAO Fei, CHEN Beng. Shadow detection based on texture feature in gray sequence images[J]. Editorial Office of Optics and Precision Engineering, 2013,21(11): 2931-2942 DOI： 10.3788/OPE.20132111.2931.

摘要

实施移动目标检测时，由于移动阴影与运动目标具有相似的运动特性，常严重影响检测结果。本文假设视频序列中当前图像的阴影区域与对应的背景部分有相同的纹理特征，提出一种新的基于纹理特征的移动阴影检测方法。为了实现纹理特征的表达，在阴影检测过程中提出了几种纹理相似度测量方法，并通过计算当前图像与背景图像的纹理相似度值来区分当前图像中阴影区域和前景目标。另外，基于Ostu算法（大津算法）提出了一种自适应的阴影分割方法，并分别对室内和室外环境下采集的视频进行了阴影检测。定性和定量分析表明，在具有显著纹理结构的视频序列下，所提出的方法对于阴影检测有很好的效果，而对于图像模糊并伴有噪声的图像检测精度会有所降低。

Abstract

Moving object detection is usually effected by moving shadows from the object and it is easy to produce serious problems because the object and the shadow have the same moving characteristic. This paper proposes a method for moving shadow detection based on texture feature under the assumption that shadow regions in the current image have the same texture feature with a corresponding background image. For the discrimination between shadows and foregrounds

several texture similarity measurements are used and the shadows and foregrounds in current image are distinguished by computing the similarity of current image and background image. Furthermore

an adaptive method for segmentation of shadows and foregrounds is presented based on Ostu algorithm. In the end

experiments are carried on the video extracted from indoor or outdoor environments. Analysis indicates that the method has a excellent detection result in a video sequence with outstanding texture features. However

detection accuracy will be reduced when the image suffers blurring or noising. The obtained results verify the efficiency of the proposed method by using both of subjective evaluation and objective evaluation.

关键词

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references

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