基于空间位置特征的运动阴影检测方法

彭祺; 仲思东; 屠礼芬; 梅天灿

doi:10.3724/SP.J.1218.2012.00614

基于空间位置特征的运动阴影检测方法

武汉大学电子信息学院,湖北武汉 430072

详细信息

作者简介:
彭祺（1983—），男，博士生．研究领域：图像测量与机器视觉．
仲思东（1963—），男，博士，教授，博士生导师．研究领域：图像测量与机器视觉．
屠礼芬（1986—），女，博士生．研究领域：图像测量与机器视觉．

通信作者:
彭祺, petersky0316@163.com

中图分类号: TP391
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出版历程
- 收稿日期: 2012-05-29
- 修回日期: 2012-08-23
- 发布日期: 2012-09-14

Moving Shadow Detection Based on Space Location Feature

School of Electronic Information, Wuhan University, Wuhan 430072, China

More Information

Corresponding author:
PENG Qi: Qi Peng

摘要

摘要: 针对现有依据光学属性检测阴影的方法对不同场景通用性不强、对光照变化敏感等问题，提出一种基于空间位置特征的运动阴影检测方法．用经过标定的双目立体摄像机同时采集背景图像像对．离线获取背景图像对中各匹配点的位置对应关系．投射阴影附着在背景表面，与背景图像像对的位置对应关系相同，故可以通过比较前景图像像对各点在背景对应位置上像素值的相似程度来剔除阴影．该方法不需对环境特征及光照条件等先验知识的学习，各种复杂光照条件均不影响其检测精度．实验表明，该方法能够有效地检测出运动阴影．
- 阴影检测 /
- 立体视觉 /
- 空间位置 /
- 运动目标 /
- 匹配
Abstract: In order to overcome the problems of poor universality and sensitivity of light change in the existing shadow detection methods based on optical properties, a moving shadow detection method based on space location feature is introduced. A calibrated binocular stereo camera is used to capture stereo background images at the same time. The position relationships between matching points from the pair of background images is calculated off-line. The cast shadows adhere to the background surface, and the matching points of that shadow have the same position relationships with that of the background stereo images, so the cast shadows can be removed by comparing the level of similarities of pixel values between the left and right images on the position of matching points in the background. Prior knowledge about neither the environment characteristics nor the lighting conditions is required in the proposed approach, and all kinds of complicated lighting conditions will not affect the detection precision. Experimental results demonstrate that this approach can effectively detect moving shadows.
- shadow detection /
- stereo vision /
- space location /
- moving object /
- matching

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参考文献(16)

[1]	Cheung S C S, Kamath C. Robust background subtraction with foreground validation for urban traffic video[J]. Eurasip Journal on Applied Signal Processing, 2005, 2005(14): 2330-2340.
[2]	Rao B, Zheng G, Chen T M, et al. An efficient hierarchical method for image shadow detection[C]//2nd International Workshop on Knowledge Discovery and Data Mining. Piscataway, NJ, USA: IEEE, 2009: 622-627.
[3]	Prati A, Mikic I, Trivedi M M, et al. Detecting moving shadows: Algorithms and evaluation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(7): 918-923.
[4]	Martel-Brisson N, Zaccarin A. Learning and removing cast shadows through a multidistribution approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(7): 1133-1146.
[5]	Tatsuya T, Atsushi S, Daisaku A, et al. Non-parametric background and shadow modeling for object detection[M]//Lecture Notes in Computer Science, vol.4843. Heidelberg, Germany: Springer-Verlag, 2007: 159-168.
[6]	Joshi A J, Papanikolopoulos N P. Learning to detect moving shadows in dynamic environments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11): 2055-2063.
[7]	Leone A, Distante C. Shadow detection for moving objects based on texture analysis[J]. Pattern Recognition, 2007, 40(4): 1222-1233.
[8]	Cucchiara R, Grana C, Piccardi M, et al. Improving shadow suppression in moving object detection with HSV color information[C]//IEEE Conference on Intelligent Transportation Systems. Piscataway, NJ, USA: IEEE, 2001: 334-339.
[9]	Zhang W, Fang X Z, Yang X K, et al. Moving cast shadows detection using ratio edge[J]. IEEE Transactions on Multimedia, 2007, 9(6): 1202-1213.
[10]	曹健,陈红倩,张凯,等.结合区域颜色和纹理的运动阴影检测方法[J].机器人,2011,33(5): 628-633. Cao J, Chen H Q, Zhang K, et al. Moving cast shadow detection based on region color and texture[J]. Robot, 2011, 33(5): 628-633.
[11]	马颂德,张正友.计算机视觉[M].北京:科学出版社,1998: 72-73. Ma S D, Zhang Z Y. Computer vision[M]. Beijing: Science Press, 1998: 72-73.
[12]	刘勇.基于图像的空间三维数据获取及建模[D].武汉:武汉大学,2004. Liu Y. 3D scene acquiring and modeling based on images[D]. Wuhan: Wuhan University, 2004.
[13]	Mei X, Sun X, Zhou M C. On building an accurate stereo matching system on graphics hardware[C]//IEEE International Conference on Computer Vision. Piscataway, NJ, USA: IEEE, 2011: 467-474.
[14]	Wang Z F, Zheng Z G. A region based stereo matching algorithm using cooperative optimization[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2008: 1-8.
[15]	仲思东,高智.序列图像编码的自动识别[J].计算机应用研究,2005(5): 148-150. Zhong S D, Gao Z. Automatic recognition of sequence image coding[J]. Application Research of Computers, 2005(5): 148-150.
[16]	Zhang X, Yang J. A novel algorithm to segment foreground from a similarly colored background[J]. International Journal of Electronics and Communications, 2009, 63(10): 831-840.