张量低秩表示和时空稀疏分解的视频前景检测

隋中山; 李俊山; 张姣; 樊少云; 孙胜永

doi:10.3788/OPE.20172402.0529

您当前的位置：

首页 >

文章列表页 >

张量低秩表示和时空稀疏分解的视频前景检测

信息科学 | 更新时间：2020-07-07

- 张量低秩表示和时空稀疏分解的视频前景检测
- Video foreground detection of tensor low-rank representation and spatial-temporal sparsity decomposition
- 光学精密工程 2017年25卷第2期页码：529-536
- 作者机构：
  
  1.火箭军工程大学信息工程系, 陕西西安 710025
  2.96518部队, 湖南怀化 418000
  3.南京炮兵学院战役战术教研室, 江苏南京 211132
- 作者简介：
  
  隋中山 (1985-), 男, 山东平度人, 博士研究生, 2007年、2009年于第二炮兵工程学院分别获得学士、硕士学位, 主要从事图像目标识别方面的研究。E-mail:zclszs@163.com SUI Zhong-shan, E-mail: zclszs@163.com
  [ "李俊山 (1956-), 男, 陕西白水人, 教授, 博士生导师, 1981于国防科技大学获得学士学位, 1988年于第二炮兵工程学院获得硕士学位, 2001年于西安微电子技术研究所获得博士学位, 主要从事智能图像处理与目标感知识别, 电子对抗模拟与仿真等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61175120)
- DOI：10.3788/OPE.20172402.0529
  中图分类号： TP391.4
- 收稿日期：2016-07-21，
  
  录用日期：2016-10-12，
  
  纸质出版日期：2017-02-25
- 稿件说明：
移动端阅览
隋中山, 李俊山, 张姣, 等. 张量低秩表示和时空稀疏分解的视频前景检测[J]. 光学精密工程, 2017,25(2):529-536.

Zhong-shan SUI, Jun-shan LI, Jiao ZHANG, et al. Video foreground detection of tensor low-rank representation and spatial-temporal sparsity decomposition[J]. Optics and precision engineering, 2017, 25(2): 529-536.
隋中山, 李俊山, 张姣, 等. 张量低秩表示和时空稀疏分解的视频前景检测[J]. 光学精密工程, 2017,25(2):529-536. DOI： 10.3788/OPE.20172402.0529.

Zhong-shan SUI, Jun-shan LI, Jiao ZHANG, et al. Video foreground detection of tensor low-rank representation and spatial-temporal sparsity decomposition[J]. Optics and precision engineering, 2017, 25(2): 529-536. DOI： 10.3788/OPE.20172402.0529.

摘要

针对视频中前景检测的问题，提出了一种基于张量低秩表示（Tensor Low-Rank Representation，TLRR）和时空稀疏分解的检测方法。由于视频序列中的前景除具有稀疏性外，本身还具有空间上的连续性以及时间上的持续性，本文提出采用时空稀疏范数对前景特性进行深入发掘。利用张量低秩表示方法将原始视频用张量形式进行分解，充分利用了原始数据的行信息和列信息，且将原始的背景、前景二分解泛化为背景、前景和噪声的三分解，使用非精确增广拉格朗日乘子（Inexact Augmented Lagrange Multiplier，IALM）方法进行最优化求解，并对算法进行了分析。设计实验对本文新方法的有效性进行了验证和比较，并对影响算法效果的重要参数ρ进行了进一步研究实验。实验结果表明：该方法能够有效检测出视频中的运动前景，其准确性相对已有方法有一定提高。

Abstract

A detection method based on Tensor Low-Rank Representation (TLRR) and spatial-temporal sparsity decomposition was proposed to detect foreground targets in video sequences. Since foreground in video sequence has sparsity inherently besides spatially continuous and temporally continuous

this paper put forward spatial-temporal sparsity-inducing norm to perform deep research on property of foreground. Original video was decomposed in tensor representation formed by tensor low-rank representation method

line information and column information of original data were fully used

and two-stage decomposition of original background and foreground was generalized to three-stage decomposition of background

foreground and noises. Optimization solution was performed with Inexact Augmented Lagrange Multiplier (IALM) method.Verification and comparison experiment was established

and further research experiment was performed to research how ρ affecting performance of algorithm. Experimental results show that the method can detect moving foreground in video effectively and improve accuracy when compared with existing methods.

关键词

Keywords

references

STAUFFER C, GRIMSON W E L. Adaptive background mixture models for real-time tracking[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins:IEEE, 1999:246-252.

ELGAMMAL A, HARWOOD D, DAVIS L. Non-parametric model for background subtraction[C]. Proc. Eur. Conf. Comput. Vis. Springer, 2000:751-767.

YASER S, MUBARAK S. Bayesian modeling of dynamic scenes for object detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(11):1778-1792.

HEIKKILA M, PIETIKAINEN M. A texture-based method for modeling the background and detecting moving objects[J]. IEEE Trans on Pattern Anal. and Machine Intelligence, 2006, 28(4):657-662.

KIM K, CHJIDABHONGSE T H, HARWOOD D, et al.. Real-time foreground-background segmentation using codebook model[J]. Real-Time Imaging, 2005, 11(3):172-185.

BOUWMANS T, ZAHZAH E H. Robust PCA via principal component pursuit:A review for a comparative evaluation in video surveillance[J]. Comput. Vis. Image Underst., 2014, 122:22-34.

BAO B K, LIU G C, XU C S, et al.. Inductive robust principal component analysis[J]. IEEE Trans on Image Processing, 2012, 21(8):3794-3800.

LIU G C, LIN Z C, YAN S C, et al.. Robust recovery of subspace structures by low-rank representation[J].IEEE Trans. Pattern Analysis and Machine Intelligence, 2013, 35(1):171-184.

LIU G C, YAN S C. Latent low-rank representation for subspace segmentation and feature extraction[C]. Proceedings of the International Conference on Computer Vision (ICCV), 2011:1615-1622.

ZHANG Z, YAN S C, ZHAO M B, et al.. Robust bilinear matrix recovery by tensor low-rank representation[C]. 2014 International Joint Conference on Neural Networks (IJCNN), 2014:2945-2951.

LIU X, ZHAO G Y. Background Subtraction Based on Low-rank and Structured Sparse Decomposition IEEE Trans on Image Processing DRAFT, 2015.

何玉杰, 李敏, 张金利, 等.基于低秩三分解的红外图像杂波抑制[J].光学精密工程. 2015, 23(7):2070-2078.

HE Y J, LI M, ZHANG J L, et al.. Clutter suppression of infrared image based on three-component low-rank matrix decomposition[J].Opt. Precision Eng., 2015, 23(7):2070-2078.(in Chinese)

YAO J W, LIU X, QI CH. Foreground detection using low rank and structured sparsity[C]. 2014 IEEE International Conference on Multimedia and Expo (ICME), 2014.

穆治亚, 魏仲慧, 何昕, 等.采用稀疏表示的红外图像自适应杂波抑制[J].光学精密工程, 2013, 21(7):1850-1857.

MU ZH Y, WEI ZH H, HE X, et al.. Adaptive clutter suppression of infrared images by using low-rank representation[J]. Opt. Precision Eng., 2013, 21(7):1850-1857.(in Chinese)

ZHANG Z, ZHAO M B, CHOW T W. Binary-and multi-class group sparse canonical correlation analysis for feature extraction and classification[J]. IEEE Trans. Knowledge and Data Engineering, 2013, 25(10):2192-2205.

赵九龙, 马瑜, 李爽, 等.三维医学图像的混合噪声去除方法[J].液晶与显示, 2015, 30(2):340-346.

ZHAO J L, MA Y, LI SH, et al.. Mixed noise removing method for three-dimensional medical images[J]. Chinese Journal of Liquid and Displays, 2015, 30(2):340-346. (in Chinese)

杨亚威, 李俊山, 张士杰, 等.基于生物视觉标准模型特征的无参考型图像质量评价方法[J].液晶与显示, 2014, 29(6):1016-1023.

YANG Y W, LI J SH, ZHANG SH J, et al.. Non-reference image quality assessment approach based on standard model features of biological vision[J]. Chinese Journal of Liquid and Displays, 2014, 29(6):1016-1023. (in Chinese)

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

动态背景下基于改进视觉背景提取的前景检测