融合区域纹理梯度的船舶阴影视频去除算法
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- 英文论文题名：Ship Shadow Elimination Algorithm Based on HSV and Texture of Gradient Amendment
- 论文作者：周东 ; 李昀峰 ; 阮雅端 ; 高妍 ; 陈启美
- 英文论文作者：ZHOU Dong ; LI Yun-feng ; Gao Yan ; RUAN Ya-duan ; CHEN Qi-mei ; School of Electronic Science and Engineering ; Nanjing University
- 年：2016
- 作者机构：南京大学电子科学与工程学院;
- 论文关键词：视频监控 ; 图像处理 ; 阴影去除 ; HSV颜色空间 ; LBP局部二值模型 ; 梯度填充
- 英文论文关键词：video surveillance ; image processing ; shadow elimination ; hsv color space ; local binary pattern ; gradient amendent
- 会议召开时间：2016-06-24
- 会议录名称：2016年全国通信软件学术会议程序册与交流文集
- 语种：中文
- 分类号：TP391.41
- 学会代码：ZGTH
- 会议名称：2016年全国通信软件学术会议
- 会议地点：中国陕西西安
- 主办单位：中国通信学会
- 学会名称：中国通信学会
- 页数：6
- 文件大小：1545k
- 原文格式：O
- 会议级别：全国
摘要
目前,借助于监控视频实时提取内河船舶运行流量等参数的船舶检测系统得以广泛应用,而传统的视频检测方法往往难以有效区分船体边界,造成检测时颜色、纹理相似的前景和阴影区域相混淆。为此,文中解析了HSV颜色特征和LBP纹理不变性提取阴影的原理,改进了SE-CT阴影去除算法,提出了基于颜色和纹理梯度特征GA-HT梯度填充的船舶阴影去除算法。测试表明,GA-HT算法能很好地去除船舶阴影,提高船舶的匹配跟踪的精度和实时性,最终阴影检测综合指标F达到92%。
At present,the monitoring system by means of real-time video has been widely used,which can extract inland parameters such as ship traffic.etc.while the main methods of shadow detection are often difficult to effectively segment the foreground and shadow because of similar color and texture information in the border region.Through analyzing the LBP texture invariance and HSV color feature extraction principle of shadow,a novel ship shadow elimination is proposed.Chromacity information is first used to create a mask of candiate shadow pixels,follwed by employing improved Local Binary Pattern(LBP) in extracting texture information to remove foregroud pixels that were incorrectly included in the mask,and then applying gradient amendent to obtain a ultimate shadow.Experiments indicate that the proposed algorithm can effectively remove the shadow of the ship,improve the ship's Matching Pursuit accuracy and real-time capability,and final shadow detection comprehensive index F is up to 92%.
At present,the monitoring system by means of real-time video has been widely used,which can extract inland parameters such as ship traffic.etc.while the main methods of shadow detection are often difficult to effectively segment the foreground and shadow because of similar color and texture information in the border region.Through analyzing the LBP texture invariance and HSV color feature extraction principle of shadow,a novel ship shadow elimination is proposed.Chromacity information is first used to create a mask of candiate shadow pixels,follwed by employing improved Local Binary Pattern(LBP) in extracting texture information to remove foregroud pixels that were incorrectly included in the mask,and then applying gradient amendent to obtain a ultimate shadow.Experiments indicate that the proposed algorithm can effectively remove the shadow of the ship,improve the ship's Matching Pursuit accuracy and real-time capability,and final shadow detection comprehensive index F is up to 92%.
引文
[1]Sanin A.Sanderson C,Lovell B C.Shadow detection:A survey and comparative evaluation of recent methods[J].Pattern Recognition,2012,45(4):1684-1695.
[2]Hsieh J W,Hu W F,Chang C J,et al.Shadow elimination for effective moving object detection by Gaussian shadow modeling[J].Image&Vision Computing,2003,21(6):505-516.
[3]Cucchiara R,Grana C,Piccardi M,et al.Detecting Moving Objects,Ghosts,and Shadows in Video Streams[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2003,25(10):1337-1342.
[4]Huang J B,Chen C S.Moving cast shadow detection using physics-based features[C].IEEE Conference on Computer Vision&Pattern Recognition.2009:2310-2317.
[5]A.Leone,C.Distante,Shadow detection for moving objects based on texture analysis,Pattern Recognition 40(4)(2007):1222-1233.
[6]Sanin A,Sanderson C,Lovell B C.Improved Shadow Removal for Robust Person Tracking in Surveillance Scenarios[C].Proceedings of the 201020th International Conference on Pattern Recognition.IEEE Computer Society,2010:141-144.
[7]董蓉,李勃,陈启美.路况视频中HSV彩色不变量阴影检测法研究与改进[J].中国图象图形学报,2009,14(12):2483-2488.Dong Rong,Li Bo,Chen Qimei.Research and Improvement on Shadow Detection in Expressway Videos Using HSV Color Model[J].Journal of Image and Graphics,2009,14(12):2483-2488.
[8]高俊祥,杜海清,刘勇.采用光照不变特征的椭球法运动阴影检测[J].北京邮电大学学报,2009,32(5):109-113.Gao Junxiang,Du Haiqing,Liu Yong.Moving Shadow Detection by Ellipsoidal Method Using Illumination Invariants[J].Journal of Beijing University of Posts and Telecommunications,2009,32(5):109-113.
[9]解文华,易本顺,肖进胜,等.基于颜色和区域梯度方向特征的阴影检测算法[J].中南大学学报:自然科学版,2013(12):4874-4880.Xie Wenhua,Yi Benshun,Xiao Jinsheng.etc.Shadow detection algorithm based on color and regional gradient direction features[J].Journal of Central South University.Science and Technology,2013(12):4874-4880.
[10]邱一川,张亚英,刘春梅.多特征融合的车辆阴影消除[J].中国图象图形学报,2015,20(3):0311-0319.Qiu Yichun,Zhang Yaying,Liu Chunmei.Vehicle shadow removal with multi-feature fusion[J]Journal of Image and Graphics.2015.20(3):0311-0319.
[11]Satpathy A,Jiang X,Eng H L.LBP-based edge-texture features for object recognition.[J],IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2014,23(5):1953-1964.
[12]Al-Najdawi N,Bez H E,Singhai J,et al.A survey of cast shadow detection algorithms[J].Pattern Recognition Letters,2012,33(6):752-764.
[13]姜柯,李艾华,苏延召.结合边缘纹理和抽样推断的自适应阴影检测算法[J].西安交通大学学报,20 13,47(2):39-46.Jiang Ke,Li Aihua,Su Yanzhao.An Adaptive Shadow Detection Algorithm Using Edge Texture and Sampling Deduction[J].Journal of Xi'an JiaotongUniversity,2013,47(2):39-46.
[14]Bernardin K,Stiefelhagen R.Evaluating Multiple Object Tracking Performance:The CLEAR MOT Metrics[J].Eurasip Journal on Image&Video Processing,2008,2008(5):1-10.
[2]Hsieh J W,Hu W F,Chang C J,et al.Shadow elimination for effective moving object detection by Gaussian shadow modeling[J].Image&Vision Computing,2003,21(6):505-516.
[3]Cucchiara R,Grana C,Piccardi M,et al.Detecting Moving Objects,Ghosts,and Shadows in Video Streams[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2003,25(10):1337-1342.
[4]Huang J B,Chen C S.Moving cast shadow detection using physics-based features[C].IEEE Conference on Computer Vision&Pattern Recognition.2009:2310-2317.
[5]A.Leone,C.Distante,Shadow detection for moving objects based on texture analysis,Pattern Recognition 40(4)(2007):1222-1233.
[6]Sanin A,Sanderson C,Lovell B C.Improved Shadow Removal for Robust Person Tracking in Surveillance Scenarios[C].Proceedings of the 201020th International Conference on Pattern Recognition.IEEE Computer Society,2010:141-144.
[7]董蓉,李勃,陈启美.路况视频中HSV彩色不变量阴影检测法研究与改进[J].中国图象图形学报,2009,14(12):2483-2488.Dong Rong,Li Bo,Chen Qimei.Research and Improvement on Shadow Detection in Expressway Videos Using HSV Color Model[J].Journal of Image and Graphics,2009,14(12):2483-2488.
[8]高俊祥,杜海清,刘勇.采用光照不变特征的椭球法运动阴影检测[J].北京邮电大学学报,2009,32(5):109-113.Gao Junxiang,Du Haiqing,Liu Yong.Moving Shadow Detection by Ellipsoidal Method Using Illumination Invariants[J].Journal of Beijing University of Posts and Telecommunications,2009,32(5):109-113.
[9]解文华,易本顺,肖进胜,等.基于颜色和区域梯度方向特征的阴影检测算法[J].中南大学学报:自然科学版,2013(12):4874-4880.Xie Wenhua,Yi Benshun,Xiao Jinsheng.etc.Shadow detection algorithm based on color and regional gradient direction features[J].Journal of Central South University.Science and Technology,2013(12):4874-4880.
[10]邱一川,张亚英,刘春梅.多特征融合的车辆阴影消除[J].中国图象图形学报,2015,20(3):0311-0319.Qiu Yichun,Zhang Yaying,Liu Chunmei.Vehicle shadow removal with multi-feature fusion[J]Journal of Image and Graphics.2015.20(3):0311-0319.
[11]Satpathy A,Jiang X,Eng H L.LBP-based edge-texture features for object recognition.[J],IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2014,23(5):1953-1964.
[12]Al-Najdawi N,Bez H E,Singhai J,et al.A survey of cast shadow detection algorithms[J].Pattern Recognition Letters,2012,33(6):752-764.
[13]姜柯,李艾华,苏延召.结合边缘纹理和抽样推断的自适应阴影检测算法[J].西安交通大学学报,20 13,47(2):39-46.Jiang Ke,Li Aihua,Su Yanzhao.An Adaptive Shadow Detection Algorithm Using Edge Texture and Sampling Deduction[J].Journal of Xi'an JiaotongUniversity,2013,47(2):39-46.
[14]Bernardin K,Stiefelhagen R.Evaluating Multiple Object Tracking Performance:The CLEAR MOT Metrics[J].Eurasip Journal on Image&Video Processing,2008,2008(5):1-10.