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moving object detection Method Based on the Fusion of Online moving Window Robust Principal Component Analysis and Frame Difference Method

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NEURAL PROCESSING LETTERS 2024年第2期56卷 55-55页

作者： Zhang, Q. L. Li, S. L. Duan, J. G. Qin, J. Y. Zhou, Y. Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 200135 Peoples R China Shanghai Maritime Univ Sch Logist Engn Shanghai 200135 Peoples R China

The accuracy of moving object detection has a great impact on the accuracy of extracting the shape center coordinates of moving workpieces. The classic inter-frame difference method has "cavity" and "double shadow" issues for workpieces with comparable internal colors. In order to solve this problem, a moving object detection algorithm combining the three-frame difference method and Online moving Window Robust Principal Component Analysis (OMWRPCA) is proposed. By using the OMWRPCA to extract the background image in the current frame and comparing it to the previous and current frames, the "cavity" and "double shadow" problems are avoided as well as the effects of background pixels. This paper presents a case study of a visual sorting experiment bench in an "intelligent manufacturing production demonstration line". The experiments show that the workpiece shape center coordinates obtained by the improved moving object detection algorithm are closer to the actual value than those obtained by the traditional algorithm, and the F-measure scores are above 0.8, which are more accurate than the other two algorithms. It is compared with the traditional algorithm of the frame difference method and the Online Mixture of Gaussian Matrix Factorization (OMoGMF).

关键词： moving object detection Improved three frame difference method Online moving window robust principal component analysis Visual sorting

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moving object detection in gigapixel-level videos using manifold sparse representation

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第6期83卷 18381-18405页

作者： Liu, Jingjing Feng, Manlong Gu, Dongzhou Zeng, Xiaoyang Liu, Wanquan Xiu, Xianchao Shanghai Univ Sch Microelect Shanghai Key Lab Chips & Syst Intelligent Connecte Shanghai Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Fudan Univ Sch Microelect State Key Lab Integrated Chips & Syst Shanghai Peoples R China Sun Yat sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China

moving object detection (MOD) is one of the most important and challenging tasks in analyzing videos. Recently, emerging gigapixel-level videos have attracted considerable attention due to its large field of view and high spatial resolution. So far, there is not much research on moving object detection in the field of gigapixel-level videos. To detect moving objects in gigapixel-level videos, we propose a novel manifold sparse representation (MSR) method for detecting moving objects in gigapixel-level videos. The innovation of this method lies in introducing sparse representation to promote global structure and utilizing manifold learning to retain the local structure of moving objects. Then, to solve the explicit solutions of this problem, an efficient optimization scheme based on the manifold alternating direction method of multipliers (MADMM) is developed. Finally, the extensive experiments on three challenging benchmark MOD datasets, three samples from the gigapixel-level PANDA dataset (gigaPixel-level humAN-centric viDeo dAtaset), and one real wide area video dataset are executed to demonstrate the superiority of the proposed MSR. In particular, the detection performance on PANDA is improved by an average of 5%, which suggests that the proposed method is promising for gigapixel-level videos.

关键词： Gigapixel-level videos Manifold sparse representation (MSR) moving object detection Optimization algorithm

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moving object detection in satellite videos based on an improved ViBe algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第3期18卷 2543-2557页

作者： Pei, Wenjing Shi, Zhanhao Gong, Kai Beihang Univ BUAA Ctr Informat & Control Sch Automat Sci & Elect Engn Beijing Peoples R China Shandong Agr & Engn Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China

This paper studies moving object detection in satellite videos, which plays a significant role for large-scale video monitoring and dynamic analysis. Due to the tiny targets, complex background, and completely or partly occlusion, moving object detection accurately from each image frame is difficult and challenging. In order to tackle the issues, we propose an improved Visual Background Extractor algorithm (ViBe) with an improved Canny edge detection operator. First, the improved Canny operator based on iteration method is employed to find the optimal high and low thresholds and extract more edges information of satellite videos. Furthermore, the eight pixels are replaced with twenty-four pixels and the times of target threshold is introduced in ViBe detection to increase the selection range of backgrounds. The experiments are tested in SATSOT datasets, where the results verify that the proposed method is more robust in complex background with interference, turning and rapid motion.

关键词： moving object detection Satellitevideos Visual background extractor Edgeinformation Complex background

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moving object detection via RPCA framework using non-convex low-rank approximation and total variational regularization

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第1期17卷 109-117页

作者： Chen, Tianfei Zhao, Dongliang Sun, Lijun Li, Shi Feng, Binbin Henan Univ Technol Minist Educ Key Lab Grain Informat Proc & Control Zhengzhou 450001 Peoples R China Henan Univ Technol Henan Key Lab Grain Photoelectr Detect & Control Zhengzhou 450001 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China

moving object detection is one of the significant tasks in computer vision. Robust Principal Component Analysis (RPCA) is a common method for moving object detection. However, this algorithm fails to effectively utilize the low-rank prior information of the background and the spatio-temporal continuity of the foreground, and exhibits degraded performance in the existence of shadows, photometric variations, rapidly moving objects, dynamic backgrounds, and jitters. Therefore, a new model via RPCA framework using non-convex low-rank approximation and total variational regularization is proposed to detect moving objects. Firstly, this paper introduces a non-convex function to deal with the problem that the nuclear norm excessively penalizes large singular values for the sake of constraining the low-rank characteristics of video background availably. Then, the sparsity is strengthened with the l(1)-norm and the spatio-temporal continuity of the foreground is explored by TV regularization. Finally, the augmented Lagrange multiplier algorithm is expanded by the alternating direction multiplier strategy to solve the model. Extensive experiments show that the proposed method outperforms existing methods in terms of the accuracy of moving object detection and foreground extraction effect.

关键词： moving object detection Non-convex rank approximation Total variational regularization Robust principal component analysis

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moving object detection method with motion regions tracking in background subtraction

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第5期17卷 2415-2423页

作者： Delibasoglu, Ibrahim Sakarya Univ Software Engn TR-54050 Sakarya Turkiye

Identifying any moving object is essential for wide-area surveillance systems and security applications. In this paper, we present a moving object detection method based on background modeling and subtraction. Background modeling-based methods describe a model with features such as color and textures to represent the background. Background subtraction is challenging due to complex background types in natural environments. Many methods suffer from numerous false detections in real applications. In this study, we create a background model with each pixel's age, mean, and variance. Our main contribution is to propose a tracking approach in background subtraction and use simple frame difference to set weight during background subtraction operation. The proposed tracking strategy aims to use spatio-temporal features in foreground mask decision. The tracking method is used as a verification mechanism for candidate moving object regions. Tracking approach is also applied for frame difference, and the generated output motion mask is used to support background model subtraction, especially for slow-moving object cases which cause failure in our background model. The main novelty of the paper is that it proposes a reasonable solution for false detection issue due to homography error without adding a heavy computational cost. We measure each module's performance to demonstrate the impact of each module on the proposed method clearly. Experimental results are examined on two publicly available aerial image datasets, PESMOD and VIVID. The proposed method runs in real time and outperforms existing background modeling-based methods. It is seen that the proposed method achieves a significant reduction in false positives and has stable performance on different kinds of images.

关键词： Background subtraction moving object detection Motion detection Foreground segmentation Surveillance

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moving object detection in low-luminance images

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VISUAL COMPUTER 2023年第1期39卷 183-195页

作者： Huang, Xiaojie Yichun Univ Yichun 336000 Jiangxi Peoples R China

moving object detection in low-luminance Images is one of the most fundamental and difficult issues in machine vision. Therefore, in this paper, deep self-adaptive network (DSA-Net) is proposed to effectively detect moving objects in low-luminance scenes. Particularly, (1) three mechanisms are developed in this joint learning framework: graph-based unsupervised feature selection, feature representations ranking, and multiple-way feature interaction. (2) Both anti-occlusion and multi-object handling module are explored simultaneously in the unified DSA-Net model. (3) A weakly fine-tuning strategy is presented, including the easiness and group curriculum term. It leverages helpful prior-knowledge to guide the learner to select confident training samples. The experimental results show that DSA-Net outperforms the state-of-the-art methods.

关键词： Deep learning moving object detection Low luminance

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moving object detection in Video Sequences Based on a Two-Frame Temporal Information CNN

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NEURAL PROCESSING LETTERS 2023年第5期55卷 5425-5449页

作者： Chacon-Murguia, Mario I. Guzman-Pando, Abimael Tecnol Nacl Mex Inst Tecnol Chihuahua Ave Tecnol 2909 CHIH Chihuahua 31310 Mexico

moving object detection methods, MOD, must solve complex situations found in video scenarios related to bootstrapping, illumination changes, bad weather, PTZ, intermittent objects, color camouflage, camera jittering, low camera frame rate, noisy videos, shadows, thermal videos, night videos, etc. Some of the most promising MOD methods are based on convolutional neural networks, which are among the best-ranked algorithms in the CDnet14 dataset. Therefore, this paper presents a novel CNN to detect moving objects called Two-Frame CNN, 2FraCNN. Unlike best-ranked algorithms in CDnet14, 2FraCNN is a non-transfer learning model and employs temporal information to estimate the motion of moving objects. The architecture of 2FraCNN is inspired by how the optical flow helps to estimate motion, and its core is the FlowNet architecture. 2FraCNN processes temporal information through the concatenation of two consecutive frames and an Encoder-Decoder architecture. 2FraCNN includes a novel training scheme to deal with unbalanced pixel classes background/foreground. 2FraCNN was evaluated using three different schemes: the CDnet14 benchmark for a state-of-the-art comparison;against human performance metric intervals for a realistic evaluation;and for practical purposes with the performance instrument PVADN that considers the quantitative criteria of performance, speed, auto-adaptability, documentation, and novelty. Findings show that 2FraCNN has a performance comparable to the top ten algorithms in CDnet14 and is one of the best twelve in the PVADN evaluation. Also, 2FraCNN demonstrated that can solve many video challenges categories with human-like performance, such as dynamic backgrounds, jittering, shadow, bad weather, and thermal cameras, among others. Based on these findings, it can be concluded that 2FraCNN is a robust algorithm solving different video conditions with competent performance regarding state-of-the-art algorithms.

关键词： moving object detection Dynamic object detection Convolutional neural network Unbalanced classes

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moving object detection for Remote Sensing Video with Satellite Jitter

Moving Object Detection for Remote Sensing Video with Satell...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Tsai, Chi-An Lee, Pei-Jun Bui, Trong-An Sheu, Meng-Lieh Natl Chi Nan Univ Elect Engn Dept Nantou Taiwan Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei Taiwan

ISBN: (纸本)9781665491303

moving object detection in dynamic scenes with satellite jitter is an important issue in remote sensing video processing. Satellite jitter leads to whole image shaking which cause difficulty for moving object detection. This paper proposed a motion vector clustering method to detect moving feature points. Real moving objects whose motion are different to jitter are able to be detected after removing jitter moving feature points. The proposed method increased 69.6% of succeed detected moving objects and reduce 93.6% of fake moving object.

关键词： satellite jitter moving object detection moving vectors

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moving object detection for unseen videos via truncated weighted robust principal component analysis and salience convolution neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第23期81卷 32779-32790页

作者： Li, Yang Jiangsu Vocat Coll Informat Technol Sch IoT Engn Sch Informat Secur 1 Qianou Rd Wuxi 214153 Jiangsu Peoples R China

moving object detection is a basic and important work in intelligent video analysis. Recently, a lot of methods have sprung up. Among them, the methods based on deep learning have achieved very amazing results. However, the methods based on deep learning rely on special annotated data to train the model. Thus they have weak generalization ability and can only deal with the data related to the training data. In order to handle this issue, this paper proposes a method based on Truncated Weighted Robust Principal Component Analysis and Salience Convolution Neural Network. Unlike other deep learning methods, the input of the proposed method does not contain the scene information. The proposed method uses the salient information obtained by the proposed Truncated Weighted Robust Principal Component Analysis as input. This improves the generalization ability of the proposed method. The experimental results show the superior performance of the proposed method for unseen videos on CDNET 2014 database.

关键词： moving object detection Convolution neural network Truncated weighted robust principal component analysis Salience Unseen videos

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moving object detection BY LOW-RANK ANALYSIS OF REGION-BASED CORRELATED MOTION FIELDS

MOVING OBJECT DETECTION BY LOW-RANK ANALYSIS OF REGION-BASED...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Kalantar, Bahareh Ueda, Naonori Zand, Mohsen Al-Najjar, Husam RIKEN Ctr Adv Intelligence Project Tokyo Japan Queens Univ Kingston ON Canada Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9798350320107

This paper proposes a novel approach for moving object detection in video sequences captured by nonstationary cameras. The approach, called RCMFD, uses region-based correlated motion fields decomposition, which exploits the sparsity of foreground motions against the low-rank structured background motion. The method uses spatial correlations of region-based features to boost accurate change detection for motion estimation, and motion features across object boundaries are used to exploit moving objects. A dense optical field, which is robust to illumination changes and noise, is established using cross-correlation of region-based features, and a robust principal component analysis (RPCA) model is applied to partition exploited motions into background and foreground motions. Experiments demonstrate the robustness of the proposed method on real video sequences.

关键词： moving object detection Optical flow Region-based Correlation-based RPCA

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