基于运动谱残差的视频显著性检测算法

doi:10.3969/j.issn.1000-3428.2014.12.046

计算机工程 ›› 2014, Vol. 40 ›› Issue (12): 247-250,257.doi: 10.3969/j.issn.1000-3428.2014.12.046

基于运动谱残差的视频显著性检测算法

刘宇光^1,2,陈耀武^1,2

1.浙江大学数字技术及仪器研究所,杭州 310027; 2.浙江省网络多媒体技术研究重点实验室,杭州 310027

收稿日期:2013-12-19 修回日期:2014-01-15 出版日期:2014-12-15 发布日期:2015-01-16
作者简介:刘宇光(1988-),男,硕士研究生,主研方向:图像处理,视频分析;陈耀武,教授、博士。
基金项目:
国家自然科学基金资助项目(40927001)

Video Saliency Detection Algorithm Based on Motion Spectral Residual

LIU Yuguang^1,2,CHEN Yaowu^1,2

1.Institute of Advanced Digital Technologies and Instrumentation,Zhejiang University,Hangzhou 310027,China;
2.Zhejiang Provincial Key Laboratory for Network Multimedia Technologies,Hangzhou 310027,China

Received:2013-12-19 Revised:2014-01-15 Online:2014-12-15 Published:2015-01-16

摘要/Abstract

摘要： 借鉴静态图像中通过计算像素灰度的谱残差检测显著性区域的方法,提出基于运动谱残差的视频显著性检测算法。通过提取每一帧的运动矢量场,对矢量场的水平和垂直分量分别计算运动谱残差,融合2个方向的运动谱残差形成运动谱残差图,将谱残差图进行顶帽变换形成视频显著图。实验结果表明,该算法能准确地分割出各种运动场景的显著性区域,在检测效果和抗噪能力方面优于现有视频显著性检测算法,适用于运动微生物检测、行人车辆检测等领域。

关键词: 视频显著性, 谱残差, 光流法, 运动矢量, 顶帽变换, 图像增强

Abstract: This paper references the idea of detecting salient area in still image through calculating spectral residual of the gray-level pixel value,so proposes a video saliency detection algorithm based on motion spectral residual.It extracts each frame’s motion vector field,calculates motion spectral residual of the horizontal and vertical components of motion vector field,merges the two components’ motion spectral residual to form a motion spectral residual image,and finally exertes a top-hat transformation on the motion spectral residual image to form video saliency image.Experimental results show that this algorithm can accurately segment saliency region out of various motion scenes,outperform existing video saliency detection algorithms in terms of detection accuracy and noise resistance.The algorithm can be applied to such field as moving microbe detection,pedestrian detection and vehicle detection.

Key words: video saliency, spectral residual, optical flow method, motion vector, top-hat transformation, image enhancement

中图分类号:

TP37

刘宇光,陈耀武. 基于运动谱残差的视频显著性检测算法[J]. 计算机工程, 2014, 40(12): 247-250,257.

LIU Yuguang,CHEN Yaowu. Video Saliency Detection Algorithm Based on Motion Spectral Residual[J]. Computer Engineering, 2014, 40(12): 247-250,257.

参考文献

[1] Itti L,Koch C,Niebur E.A Model of Saliency-based Visual Attention for Rapid Scene Analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(11):1254-1259.
[2] Harel J,Koch C,Perona P.Graph-based Visual Saliency[C]//Proceedings of NIPS’06.[S.l.]:IEEE Press,2006:545-552.
[3] Hou Xiaodi,Zhang Liqing.Saliency Detection:A Spectral Residual Approach[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Minneapolis,USA:IEEE Press,2007:1-7.
[4] Cheng Mingming,Zhang Guoxin,Mitra N J,et al.Global Contrast Based Salient Region Detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:Springer,2011:409-416.
[5] Perazzi F,Krahenbuhl P,Pritch Y,et al.Saliency Filters:Contrast Based Filtering for Salient Region Detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Providence,USA:IEEE Press,2012:733-740.
[6] Yang Chuan,Zhang Lihe,Lu Huchuan,et al.Saliency Detection via Graph-based Manifold Ranking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Providence,USA:IEEE Press,2012:3166-3173.
[7] Zivkovic Z.Improved Adaptive Gaussian Mixture Model for Background Subtraction[C]//Proceedings of IEEE Conference on Pattern Recognition.Cambridge,UK:IEEE Press,2004:28-31.
[8] Zhai Yun,Shah M.Visual Attention Detection in Video Sequences Using Spatiotemporal Cues[C]//Proceedings of the 14th Annual ACM International Conference on Multimedia.New York,USA:ACM Press,2006:815-824.
[9] Guo Chenlei,Ma Qi,Zhang Liming.Spatio-temporal Saliency Detection Using Phase Spectrum of Quaternion Fourier Transform[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE Press,2008:1-8.
[10] Cui Xinyi,Liu Qingshan,Metaxas D.Temporal Spectral Residual:Fast Motion Saliency Detection[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE Press,2009:617-620.
[11] Sun Deqing,Roth S,Black M J.Secrets of Optical Flow Estimation and Their Principles[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.San Francisco,USA:[s.n.],2010:2432-2439.
[12] 张雯,葛玉荣.基于形态学与不完全树形小波分解的图像纹理特征提取算法[J].计算机应用,2011,31(6):1592-1594.
[13] Achanta R,Hemami S,Estrada F,et al.Frequency-tuned Salient Region Detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Miami,USA:IEEE Press,2009:1597-1604.

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基于运动谱残差的视频显著性检测算法

Video Saliency Detection Algorithm Based on Motion Spectral Residual

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