结合<i>l</i><sub>1/2</sub>范数与显著性约束的背景减除

doi:10.19678/j.issn.1000-3428.0061356

计算机工程 ›› 2022, Vol. 48 ›› Issue (6): 263-269. doi: 10.19678/j.issn.1000-3428.0061356

结合l_1/2范数与显著性约束的背景减除

张国庭¹, 陈利霞^1,2, 周泽锋¹

1. 桂林电子科技大学数学与计算科学学院, 广西桂林 541004;
2. 桂林电子科技大学广西高校数据分析与计算重点实验室, 广西桂林 541004

收稿日期:2021-04-02 修回日期:2021-07-09 发布日期:2021-08-11
作者简介:张国庭(1994—),男,硕士研究生,主研方向为目标检测;陈利霞(通信作者),教授;周泽锋,本科生。
基金资助:
国家自然科学基金（11961010）；广西自然科学基金（2018GXNSFAA138169）。

Background Subtraction Combining l_1/2 Norm and Saliency Constraint

ZHANG Guoting¹, CHEN Lixia^1,2, ZHOU Zefeng¹

1. School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China;
2. Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China

Received:2021-04-02 Revised:2021-07-09 Published:2021-08-11

摘要/Abstract

摘要： 传统背景减除模型在背景静止和前景对象移动较快时提取到的前景效果较好，但当背景变化或前景对象移动缓慢时容易将动态背景误判为前景或检测出的前景有较多空洞。针对传统背景减除模型在动态背景和前景对象移动缓慢条件下存在前景检测精度下降的问题，提出一种基于l_1/2范数与显著性约束的背景减除模型。将观测数据分为低秩背景、运动前景和动态干扰3类，利用l_1/2范数约束运动前景加强前景稀疏性，有效抑制动态背景对前景提取造成的干扰，提高运动前景在动态背景中的检测精度。引入视频每一帧的显著性约束，通过对每一帧图像进行低秩稀疏分解来检测移动缓慢的目标。实验结果表明，该模型对于复杂场景具有较强的适应能力，可有效去除动态背景对前景的干扰，快速检测出移动缓慢的前景对象，相比于l_1/1/2-RPCA背景减除模型的平均查全率、查准率和调和平均值分别提升了9、14和10个百分点。

关键词: 低秩稀疏分解, 前景检测, l_1/2范数, 显著性约束, 背景减除

Abstract: The conventional background subtraction model extracts the foreground more effectively when the background is static and the foreground object propagates rapidly.However, when the background is dynamic or the foreground object propagates slowly, the dynamic background can be easily misjudged as the foreground or more foreground voids are detected.To address the decreasing foreground detection accuracy of conventional background subtraction models in cases involving a dynamic background and slowly propagating foreground objects, a background subtraction model based on the l_1/2 norm and saliency constraints is proposed.The observation data are classified into three types:low-rank background, motion foreground, and dynamic interference.The l_1/2 norm is used to constrain the motion foreground and hence strengthen the foreground sparsity, which effectively suppresses the interference caused by the dynamic background to the foreground extraction and improves the performance of the motion foreground in the dynamic background.A saliency constraint for each frame of video is introduced, and slowly propagating objects are detected by performing low-rank sparse decomposition on each frame of images.Experimental results show that the model is highly adaptable to complex scenes, effectively removes the interference of dynamic background on the foreground, and detects slowly propagating foreground objects rapidly.Compared with the l_1/1/2-RPCA background subtraction model, the proposed model improves the average recall, precision, and F-measure by 9, 14, and 10 percentage points, respectively.

Key words: low-rank and sparse decomposition, foreground detection, l_1/2 norm, saliency constraint, background subtraction

中图分类号:

TP391

张国庭, 陈利霞, 周泽锋. 结合l_1/2范数与显著性约束的背景减除[J]. 计算机工程, 2022, 48(6): 263-269.

ZHANG Guoting, CHEN Lixia, ZHOU Zefeng. Background Subtraction Combining l_1/2 Norm and Saliency Constraint[J]. Computer Engineering, 2022, 48(6): 263-269.

https://www.ecice06.com/CN/Y2022/V48/I6/263

图/表 6

20220625181124

20220625181128

20220625181131

20220625181135

20220625181139

20220625181142

参考文献

[1] YANG J Y, SHI W, YUE H J, et al.Spatiotemporally scalable matrix recovery for background modeling and moving object detection[J].Signal Processing, 2020, 168:107362.
[2] JU J G, XING J S.Moving object detection based on smoothing three frame difference method fused with RPCA[J].Multimedia Tools and Applications, 2019, 78(21):29937-29951.
[3] YANG Z Z, FAN L, YANG Y P, et al.Generalized singular value thresholding operator based nonconvex low-rank and sparse decomposition for moving object detection[J].Journal of the Franklin Institute, 2019, 356(16):10138-10154.
[4] CHEN Z, WANG R L, ZHANG Z, et al.Background-foreground interaction for moving object detection in dynamic scenes[J].Information Sciences, 2019, 483:65-81.
[5] BOUWMANS T, ZAHZAH E H.Robust PCA via principal component pursuit:a review for a comparative evaluation in video surveillance[J].Computer Vision and Image Understanding, 2014, 122:22-34.
[6] STAUFFER C, GRIMSON W E L.Adaptive background mixture models for real-time tracking[C]//Proceedings of 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 1999:246-252.
[7] TUZEL O, PORIKLI F, MEER P.A Bayesian approach to background modeling[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2005:1-8.
[8] ZIVKOVIC Z, VAN DER HEIJDEN F.Efficient adaptive density estimation per image pixel for the task of background subtraction[J].Pattern Recognition Letters, 2006, 27(7):773-780.
[9] 邵奇可, 周宇, 李路, 等.复杂场景下自适应背景减除算法[J].中国图象图形学报, 2015, 20(6):756-763. SHAO Q K, ZHOU Y, LI L, et al.Adaptive background subtraction approach of Gaussian mixture model[J].Journal of Image and Graphics, 2015, 20(6):756-763.(in Chinese)
[10] CAO X C, YANG L, GUO X J.Total variation regularized RPCA for irregularly moving object detection under dynamic background[J].IEEE Transactions on Cybernetics, 2016, 46(4):1014-1027.
[11] EBADI S E, IZQUIERDO E.Foreground segmentation with tree-structured sparse RPCA[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(9):2273-2280.
[12] CHEN L X, LIU J L, WANG X W.Background subtraction with Kronecker-basis-representation based tensor sparsity and l_1/1/2norm[J].Multidimensional Systems and Signal Processing, 2021, 32(1):77-90.
[13] LI Y, LIU G C, LIU Q S, et al.Moving object detection via segmentation and saliency constrained RPCA[J].Neurocomputing, 2019, 323:352-362.
[14] PANG Y W, YE L, LI X L, et al.Incremental learning with saliency map for moving object detection[J].IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(3):640-651.
[15] TOM A J, GEORGE S N.A three-way optimization technique for noise robust moving object detection using tensor low-rank approximation, l_1/2, and TTV regularizations[J].IEEE Transactions on Cybernetics, 2021, 51(2):1004-1014.
[16] CANDES E J, LI X, MA Y, et al.Robust principal component analysis?[J].Journal of the ACM, 2011, 58(3):11.
[17] WANG Y, JODOIN P M, PORIKLI F, et al.CDnet 2014:an expanded change detection benchmark dataset[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops.Washington D.C., USA:IEEE Press, 2014:393-400.
[18] XIE Q, ZHAO Q, MENG D Y, et al.Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(8):1888-1902.
[19] GOLDFARB D, QIN Z T.Robust low-rank tensor recovery:models and algorithms[J].SIAM Journal on Matrix Analysis and Applications, 2014, 35(1):225-253.
[20] REZAEI B, OSTADABBAS S.Background subtraction via fast robust matrix completion[C]//Proceedings of IEEE International Conference on Computer Vision Workshops.Washington D.C., USA:IEEE Press, 2017:1871-1879.
[21] LU C Y, FENG J S, CHEN Y D, et al.Tensor robust principal component analysis with a new tensor nuclear norm[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(4):925-938.
[22] LIN Z, CHEN M, MA Y.The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices[J].Journal of Structural Biology, 2010, 181(2):116-127.

选择文件类型/文献管理软件名称

选择包含的内容

结合l_1/2范数与显著性约束的背景减除

Background Subtraction Combining l_1/2 Norm and Saliency Constraint

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献

相关文章 11

编辑推荐

Metrics

本文评价

[1]	潘金凤, 尹丽菊, 高明亮, 邹国峰. 压缩感知观测信号的低秩稀疏分解[J]. 计算机工程, 2022, 48(8): 234-239.
[2]	鹿天然,于凤芹,陈莹. 一种基于线性序列差异分析降维的人体行为识别方法[J]. 计算机工程, 2019, 45(3): 237-241,249.
[3]	夏森林,孙怀江,陈贝佳. Welsch M-估计在视频重构与背景减除中的应用[J]. 计算机工程, 2019, 45(1): 270-277.
[4]	杨杰,赵敏,林亮,苏浩. 基于多轨迹矢量搜索的射流轨迹识别方法[J]. 计算机工程, 2015, 41(7): 280-284.
[5]	叶勇, 管业鹏, 李晶晶. 基于混合高斯模型与码本算法的前景目标检测[J]. 计算机工程, 2012, 38(5): 1-4.
[6]	韩萍, 罗的国. 均值漂移目标跟踪的两级窗宽更新算法[J]. 计算机工程, 2012, 38(12): 158-161.
[7]	王炜, 钱徽, 陈鹏, 金卓军. 改进的Online EM背景减除算法[J]. 计算机工程, 2011, 37(4): 201-202.
[8]	王琛, 郭继昌, 冯晓敏. 基于双层背景的遗弃物检测方法[J]. 计算机工程, 2011, 37(24): 167-168.
[9]	冯华文, 龚声蓉, 刘纯平. 基于改进高斯混合模型的前景检测[J]. 计算机工程, 2011, 37(19): 179-182.
[10]	徐中宇, 姜洪霖, 张忠波. 基于改进C-V模型的步态轮廓提取方法[J]. 计算机工程, 2010, 36(17): 182-183,186.
[11]	黄　薇;肖　平;冯　刚. 带有阴影消除的室内运动人体的提取与跟踪[J]. 计算机工程, 2007, 33(05): 170-172.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

结合l1/2范数与显著性约束的背景减除

Background Subtraction Combining l1/2 Norm and Saliency Constraint

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献

相关文章 11

编辑推荐

Metrics

本文评价

结合l_1/2范数与显著性约束的背景减除

Background Subtraction Combining l_1/2 Norm and Saliency Constraint