Dim small targets detection based on statistical block low-rank background modeling

2019 ◽  
Author(s):  
Li Biao ◽  
Xu Zhiyong ◽  
Zhang Jianlin ◽  
Fan Xiangsuo
Keyword(s):  
Background Modeling ◽  
Low Rank ◽  
Small Targets

scholarly journals Background Modeling Based on Statistical Clustering Partitioning

2021 ◽  
Vol 2021 ◽  
pp. 1-28
Author(s):  
Biao Li ◽  
Xu Zhiyong ◽  
Jianlin Zhang ◽  
Xiangru Wang ◽  
Xiangsuo Fan
Keyword(s):  
Sparse Matrix ◽  
Background Modeling ◽  
Low Rank ◽  
Background Suppression ◽  
Detection Rates ◽  
Residual Noise ◽  
Video Frames ◽  
The Difference ◽  
Small Targets ◽  
Statistical Clustering

In order to effectively detect dim-small targets in complex scenes, background suppression is applied to highlight the targets. This paper presents a statistical clustering partitioning low-rank background modeling algorithm (SCPLBMA), which clusters the image into several patches based on image statistics. The image matrix of each patch is decomposed into low-rank matrix and sparse matrix in the SCPLBMA. The background of the original video frames is reconstructed from the low-rank matrices, and the targets can be obtained by subtracting the background. Experiments on different scenes show that the SCPLBMA can effectively suppress the background and textures and equalize the residual noise with gray levels significantly lower than that of the targets. Thus, the difference images obtain good stationary characteristics, and the contrast between the targets and the residual backgrounds is significantly improved. Compared with six other algorithms, the SCPLBMA significantly improved the target detection rates of single-frame threshold segmentation.

scholarly journals Robust Principal Component Analysis-Based Infrared Small Target Detection

2019 ◽  
Vol 33 ◽  
pp. 9925-9926
Author(s):  
Qiwei Chen ◽  
Cheng Wu ◽  
Yiming Wang
Keyword(s):  
Iteration Process ◽  
Principal Component ◽  
Component Analysis ◽  
Low Rank ◽  
Small Target ◽  
Frame Sequence ◽  
Augmented Lagrange Multiplier ◽  
Small Targets ◽  
Infrared Small Target ◽  
Augmented Lagrange Multiplier Method

A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.

scholarly journals Infrared Weak-Small Targets Fusion Based on Latent Low-Rank Representation and DWT

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 112681-112692
Author(s):  
Xiaozhu Wang ◽  
Jianfei Yin ◽  
Kai Zhang ◽  
Shayi Li ◽  
Jie Yan
Keyword(s):  
Low Rank ◽  
Low Rank Representation ◽  
Small Targets

scholarly journals Optimization of the Regularization in Background and Foreground Modeling

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Si-Qi Wang ◽  
Xiang-Chu Feng
Keyword(s):  
Background Modeling ◽  
Low Rank ◽  
Data Matrix ◽  
Video Sequences ◽  
Approximation Model ◽  
Sparse Model ◽  
Detailed Explanation ◽  
Novel Model ◽  
Typical Method ◽  
Better Than

Background and foreground modeling is a typical method in the application of computer vision. The current general “low-rank + sparse” model decomposes the frames from the video sequences into low-rank background and sparse foreground. But the sparse assumption in such a model may not conform with the reality, and the model cannot directly reflect the correlation between the background and foreground either. Thus, we present a novel model to solve this problem by decomposing the arranged data matrixDinto low-rank backgroundLand moving foregroundM. Here, we only need to give the priori assumption of the background to be low-rank and let the foreground be separated from the background as much as possible. Based on this division, we use a pair of dual norms, nuclear norm and spectral norm, to regularize the foreground and background, respectively. Furthermore, we use a reweighted function instead of the normal norm so as to get a better and faster approximation model. Detailed explanation based on linear algebra about our two models will be presented in this paper. By the observation of the experimental results, we can see that our model can get better background modeling, and even simplified versions of our algorithms perform better than mainstream techniques IALM and GoDec.

scholarly journals Background modeling from video sequences via online motion-aware RPCA

2021 ◽  
pp. 29-29
Author(s):  
Xu Weiyao ◽  
Xia Ting ◽  
Jing Changqiang
Keyword(s):  
Principal Component ◽  
Background Modeling ◽  
Low Rank ◽  
Video Frame ◽  
Robust Principal Component Analysis ◽  
Online Application ◽  
Rank Matrix ◽  
Computer Vision Applications ◽  
Low Rank Matrix ◽  
Nuclear Norm Regularization

Background modeling of video frame sequences is a prerequisite for computer vision applications. Robust principal component analysis(RPCA), which aims to recover low rank matrix in applications of data mining and machine learning, has shown improved background modeling performance. Unfortunately, The traditional RPCA method considers the batch recovery of low rank matrix of all samples, which leads to higher storage cost. This paper proposes a novel online motion-aware RPCA algorithm, named OM-RPCAT, which adopt truncated nuclear norm regularization as an approximation method for of low rank constraint. And then, Two methods are employed to obtain the motion estimation matrix, the optical flow and the frame selection, which are merged into the data items to separate the foreground and background. Finally, an efficient alternating optimization algorithm is designed in an online manner. Experimental evaluations of challenging sequences demonstrate promising results over state-of-the-art methods in online application.

scholarly journals Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Fan Xiangsuo ◽  
Xu Zhiyong
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Background Modeling ◽  
Small Target ◽  
Local Energy ◽  
Dynamic Scenes ◽  
Detection Ability ◽  
Small Target Detection ◽  
The Difference ◽  
Small Targets

In order to improve the detection ability of dim and small targets in dynamic scenes, this paper first proposes an anisotropic gradient background modeling method combined with spatial and temporal information and then uses the multidirectional gradient maximum of neighborhood blocks to segment the difference maps. On the basis of previous background modeling and segmentation extraction candidate targets, a dim small target detection algorithm for local energy aggregation degree of sequence images is proposed. Experiments show that compared with the traditional algorithm, this method can eliminate the interference of noise to the target and improve the detection ability of the system effectively.

Small Target Detection for Infrared Image Based on Optimal Infrared Patch-Image Model by Solving Modified Adaptive RPCA Problem

2020 ◽  
pp. 2150007
Author(s):  
Bin Xiong ◽  
Xinhan Huang ◽  
Min Wang ◽  
Gang Peng
Keyword(s):  
Target Detection ◽  
Principal Component ◽  
Low Rank ◽  
Small Target ◽  
Image Model ◽  
Threshold Method ◽  
Small Target Detection ◽  
Augmented Lagrange Multiplier ◽  
Small Targets ◽  
Patch Image

Small target detection in infrared (IR) images has been widely applied for both military and civilian purposes. In this study, because IR images contain sparse and low-rank features in most scenarios, we propose an optimal IR patch-image (OIPI) model-based detection method to detect small targets in heavily cluttered IR images. First, the OIPI model was generated based on a conventional IR image model using a novel optimal patch size and sliding step adaptive selection algorithm. Secondly, the sparse and low-rank features of IR images were extracted and fused to generate an adaptive weighted parameter. Thirdly, the adaptive inexact augmented Lagrange multiplier (AIALM) algorithm was applied in the OIPI model to solve the robust principal component analysis (RPCA) optimization problem. Finally, an adaptive threshold method is proposed to segment and calibrate targets. Experimental results indicate that the proposed algorithm is capable of detecting small targets more stably and accurately, compared with state-of-the-art methods.

Research on Background Modeling Based on Low-Rank Matrix Recovery

2014 ◽  
Vol 635-637 ◽  
pp. 1056-1059 ◽  
Author(s):  
Bao Yan Wang ◽  
Xin Gang Wang
Keyword(s):  
Sparse Matrix ◽  
Background Modeling ◽  
Low Rank ◽  
Complex Scene ◽  
Matrix Recovery ◽  
Rank Matrix ◽  
Training Samples ◽  
The Individual ◽  
Low Rank Matrix Recovery ◽  
Low Rank Matrix

Key and difficult points of background subtraction method lie in looking for an ideal background modeling under complex scene. Stacking the individual frames as columns of a big matrix, background parts can be viewed as a low-rank background matrix because of large similarity among individual frames, yet foreground parts can be viewed as a sparse matrix as foreground parts play a small role in individual frames. Thus the process of video background modeling is in fact a process of low-rank matrix recovery. Background modeling based on low-rank matrix recovery can separate foreground images from background at the same time without pre-training samples, besides, the approach is robust to illumination changes. However, there exist some shortcomings in background modeling based on low-rank matrix recovery by analyzing numerical experiments, which is developed from three aspects.

Sign in / Sign up

Export Citation Format

Share Document