scholarly journals Video Foreground Detection Algorithm Based on Fast Principal Component Pursuit and Motion Saliency

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Chen ◽  
Ying Tong ◽  
Jie Yang ◽  
Minghu Wu
Keyword(s):  
Principal Component ◽  
Detection Algorithm ◽  
Low Rank ◽  
Foreground Detection ◽  
Dynamic Background ◽  
Foreground Region ◽  
Principal Component Pursuit ◽  
Small Targets ◽  
Background Motion ◽  
Motion Saliency

Aiming at the shortcoming of being unsuitable for dynamic background and high computational complexity of the existing RPCA- (robust principal component analysis-) based block-sparse moving object detection method, this paper proposes a two-stage foreground detection framework based on motion saliency for video sequence. At the first stage, the observed image sequence 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 method via fast PCP (principal component pursuit). At the second stage, the sparse foreground blocks are obtained according to the spectral residuals and the spatial correlation of the foreground region. Finally, the block-sparse RPCA algorithm through fast PCP is used to estimate foreground areas dynamically and to reconstruct the foreground objects. Extensive experiments demonstrate that our method can exclude the interference of background motion and change, simultaneously improving the detection rate of small targets.

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.

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.

scholarly journals Restricted Isometry Property of Principal Component Pursuit with Reduced Linear Measurements

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Qingshan You ◽  
Qun Wan ◽  
Haiwen Xu
Keyword(s):  
Sparse Matrix ◽  
Principal Component ◽  
Restricted Isometry Property ◽  
Low Rank ◽  
Linear Measurements ◽  
Matrix Recovery ◽  
Strongly Convex ◽  
Rank Matrix ◽  
Principal Component Pursuit ◽  
Low Rank Matrix Recovery

The principal component prsuit with reduced linear measurements (PCP_RLM) has gained great attention in applications, such as machine learning, video, and aligning multiple images. The recent research shows that strongly convex optimization for compressive principal component pursuit can guarantee the exact low-rank matrix recovery and sparse matrix recovery as well. In this paper, we prove that the operator of PCP_RLM satisfies restricted isometry property (RIP) with high probability. In addition, we derive the bound of parameters depending only on observed quantities based on RIP property, which will guide us how to choose suitable parameters in strongly convex programming.

scholarly journals Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Gustavo Chau ◽  
Paul Rodríguez
Keyword(s):  
Reference Frame ◽  
Matrix Method ◽  
Moving Objects ◽  
State Of The Art ◽  
Principal Component ◽  
Background Modeling ◽  
The State ◽  
Low Rank ◽  
Current Reference ◽  
Principal Component Pursuit

Video background modeling is an important preprocessing stage for various applications, and principal component pursuit (PCP) is among the state-of-the-art algorithms for this task. One of the main drawbacks of PCP is its sensitivity to jitter and camera movement. This problem has only been partially solved by a few methods devised for jitter or small transformations. However, such methods cannot handle the case of moving or panning cameras in an incremental fashion. In this paper, we greatly expand the results of our earlier work, in which we presented a novel, fully incremental PCP algorithm, named incPCP-PTI, which was able to cope with panning scenarios and jitter by continuously aligning the low-rank component to the current reference frame of the camera. To the best of our knowledge, incPCP-PTI is the first low-rank plus additive incremental matrix method capable of handling these scenarios in an incremental way. The results on synthetic videos and Moseg, DAVIS, and CDnet2014 datasets show that incPCP-PTI is able to maintain a good performance in the detection of moving objects even when panning and jitter are present in a video. Additionally, in most videos, incPCP-PTI obtains competitive or superior results compared to state-of-the-art batch methods.

Principal Component Pursuit with Weighted Nuclear Norm

2014 ◽  
Vol 513-517 ◽  
pp. 1722-1726
Author(s):  
Qing Shan You ◽  
Qun Wan
Keyword(s):  
Sparse Matrix ◽  
Principal Component ◽  
Low Rank ◽  
Linear Measurements ◽  
Alternating Direction ◽  
Rank Matrix ◽  
Small Set ◽  
Principal Component Pursuit ◽  
Low Dimensional ◽  
Low Rank Matrix

Principal Component Pursuit (PCP) recovers low-dimensional structures from a small set of linear measurements, such as low rank matrix and sparse matrix. Pervious works mainly focus on exact recovery without additional noise. However, in many applications the observed measurements are corrupted by an additional white Gaussian noise (AWGN). In this paper, we model the recovered matrix the sum a low-rank matrix, a sparse matrix and an AWGN. We propose a weighted PCP for the recovery matrix, which is solved by alternating direction method. Numerical results show that the reconstructions performance of weighted PCP outperforms the classical PCP in term of accuracy.

scholarly journals Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

2021 ◽  
Vol 13 (10) ◽  
pp. 1930
Author(s):  
Gabriel Loureiro ◽  
André Dias ◽  
Alfredo Martins ◽  
José Almeida
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Detection Algorithm ◽  
Cloud Data ◽  
Safety Issues ◽  
Spot Detection ◽  
Cloud Clusters ◽  
Aerial Vehicle ◽  
Incoming Light ◽  
Landing Spot

The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

Computing Robust Principal Components by A* Search

2018 ◽  
Vol 27 (07) ◽  
pp. 1860013 ◽  
Author(s):  
Swair Shah ◽  
Baokun He ◽  
Crystal Maung ◽  
Haim Schweitzer
Keyword(s):  
State Of The Art ◽  
Principal Component ◽  
Low Rank ◽  
A Algorithm ◽  
Running Time ◽  
Current State ◽  
Dimensionality Reduction Technique ◽  
Related Variant ◽  
Low Rank Representation ◽  
The Cost

Principal Component Analysis (PCA) is a classical dimensionality reduction technique that computes a low rank representation of the data. Recent studies have shown how to compute this low rank representation from most of the data, excluding a small amount of outlier data. We show how to convert this problem into graph search, and describe an algorithm that solves this problem optimally by applying a variant of the A* algorithm to search for the outliers. The results obtained by our algorithm are optimal in terms of accuracy, and are shown to be more accurate than results obtained by the current state-of-the- art algorithms which are shown not to be optimal. This comes at the cost of running time, which is typically slower than the current state of the art. We also describe a related variant of the A* algorithm that runs much faster than the optimal variant and produces a solution that is guaranteed to be near the optimal. This variant is shown experimentally to be more accurate than the current state-of-the-art and has a comparable running time.

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