检索结果-内蒙古大学图书馆

moving objects segmentation using generative adversarial modeling

NEUROCOMPUTING 2022年第0期506卷 240-251页

作者： Sultana, Maryam Mahmood, Arif Bouwmans, Thierry Khan, Muhammad Haris Jung, Soon Ki Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Informat Technol Univ ITU Dept Comp Sci Lahore Pakistan Rochelle Univ Lab MIA La Rochelle France Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea

moving objects segmentation (MOS) is a crucial step in various computer vision applications, such as visual object tracking, autonomous vehicles, human activity analysis, surveillance, and security. Existing MOS approaches suffer from performance degradation due to extreme challenging conditions in real world complex environments such as varying illumination conditions, camouflage objects, dynamic backgrounds, shadows, bad weathers and camera jitters. To address these problems we pro-posed a novel generative adversarial based framework for moving objects segmentation. Our framework works with one classifier discriminator, one representation learning network and one generator jointly trained to perform MOS in various challenging scenarios. During training the discriminator network acts as a decision maker between real and fake training samples using conditional least squares loss. While the representation learning network provides the difference between the deep features of real and fake training samples using content loss formulation. Another loss term we have exploited to train our gen-erator network is the reconstruction loss that minimizes the difference between the spatial information of real and fake training samples. Moreover, we also propose a novel modified U-net architecture for our generator network showing improved performance over Vanilla U-net model. Experimental evaluations of our proposed method on four benchmark datasets in comparison with thirty-two existing methods has demonstrated the strength of our proposed model.(c) 2022 Elsevier B.V. All rights reserved.

关键词： moving objects segmentation Generative adversarial network Background modelling

来源：评论

学校读者我要写书评

暂无评论

Adaptive Graph-cut Algorithm to Video moving objects segmentation

Adaptive Graph-cut Algorithm to Video Moving Objects Segment...

引用

2nd International Congress on Image and Signal Processing

作者： Guo Chun-sheng Wang Pan Hangzhou Dianzi Univ Coll Commun Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781424441303

An adaptive Graph-cut algorithm to video moving objects segmentation was proposed. By the Kalman prediction of the number of objectives pixels and objectives-background pixel-pairs, and adaptive updating of the nodes flux, the Graph-cut algorithm was successfully applied to video moving objects segmentation. It was achieve to continuous global optimization segmentation of video moving objects. Experimental results show that the quantitative detection parameters of this algorithm are performing well in complex background conditions.

关键词： moving objects segmentation Graph-cut Markov random field Kalman prediction

来源：评论

学校读者我要写书评

暂无评论

segmentation and tracking of moving objects for content-based video coding

引用

IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING 1999年第3期146卷 144-150页

作者： Meier, T Ngan, KN Univ Western Australia Dept Elect & Elect Engn Visual Commun Res Grp Perth WA 6907 Australia

To enable content-based functionalities in video coding, a decomposition of the scene into physical objects is required. Such objects are normally not characterised by homogeneous colour, intensity, or optical flow Therefore, conventional techniques based on these low-level features cannot perform the desired segmentation. The authors address segmentation and tracking of moving objects and present a new video object plane (VOP) segmentation algorithm that extracts semantically meaningful objects. A morphological motion filter detects physical objects by identifying areas that are moving differently from the background. A new filter criterion is introduced that measures the deviation of the estimated local motion from the synthesised global motion. A two-dimensional binary model is derived for the object of interest and tracked throughout the sequence by a Hausdorff object tracker. To accommodate for rotations and changes in shape, the model is updated every frame by a two-stage method that accounts for rigid and non-rigid moving parts of the object. The binary model then guides the actual VOP extraction, whereby a novel boundary post-processor ensures high boundary accuracy. Experimental results demonstrate the performance of the proposed algorithm.

关键词： image sequence moving objects segmentation video coding motion estimation high boundary accuracy rigid moving parts scene decomposition feature extraction image segmentation Hausdorff object tracker Filtering methods in signal processing content-based video coding Video signal processing moving objects tracking two-dimensional binary model edge detection filter criterion morphological motion filter local motion estimation boundary post-processor performance Image recognition tracking Image and video coding nonrigid moving parts filtering theory synthesised global motion image sequences video object plane VOP segmentation algorithm experimental results

来源：评论

学校读者我要写书评

暂无评论

moving vehicles segmentation based on Bayesian framework for Gaussian motion model

引用

PATTERN RECOGNITION LETTERS 2006年第9期27卷 956-967页

作者： Zhang, Wei Fang, Xiang Zhong Yang, Xiaokang Shanghai Jiao Tong Univ Dept Elect Inst Image Commun & Informat Proc Shanghai 200240 Peoples R China

Accurate moving vehicles segmentation is an interesting yet difficult problem in intelligent transportation system. Robust segmentation is obstructed by some problems: the interferential moving objects in dynamic scenes, the vibration of cameras and the change of lighting conditions are all examples. In this paper, we propose Gaussian motion model for moving vehicles segmentation in the dynamic scenes. By investigating the distinction between the motion vectors of the dynamic background and those of the moving vehicles, we find the fact that the motion vectors of the moving vehicles cluster in a small region while those of the dynamic background are dispersive. Gaussian motion model is then proposed to model the motion of the moving vehicles and that of the dynamic background. Bayesian framework is utilized to classify the motion in the scenes to improve the robustness of the model and EM algorithm is used to estimate the parameters of the model. Experimental results on typical scenes, such as waving trees, camera vibration, snowfall and fog, show that the proposed model can segment the moving vehicles correctly in the complex scenes. Quantitative evaluations demonstrate that the proposed method outperform the existing methods. (c) 2006 Elsevier B.V. All rights reserved.

关键词： Gaussian motion model moving objects segmentation intelligent transportation system vehicles detection

来源：评论

学校读者我要写书评

暂无评论

moving vehicles detection based on adaptive motion histogram

引用

DIGITAL SIGNAL PROCESSING 2010年第3期20卷 793-805页

作者： Zhang, Wei Wu, Q. M. Jonathan Yin, Hai Bing Univ Windsor CVSSL Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Peking Univ Inst Digital Media Beijing Peoples R China China Jiliang Univ Sch Elect Engn Dept Informat Engn Hangzhou Zhejiang Peoples R China

As one of the important topics in computer vision, moving vehicle segmentation has attracted considerable attention of researchers. However, robust detection is hampered by the interferential moving objects in dynamic scenes. In this paper, we address the problem of the moving vehicles segmentation in the dynamic scenes. Based on the distinct motion property of the dynamic background and that of the moving vehicles, we present an adaptive motion histogram for moving vehicles segmentation. The presented algorithm consists of two procedures: adaptive background update and motion histogram-based vehicles segmentation. In the adaptive background update procedure, we make use of the lighting change of the scene and present a novel method for background evolving. In the motion histogram-based vehicles segmentation procedure, an adaptive motion histogram is maintained and updated according to the motion information in the scenes, and the moving vehicles are then detected according to the motion histogram maintained. Experimental results of typical scenes demonstrate robustness of the proposed method. Quantitative evaluation and comparison with the existing methods show that the proposed method provides much improved results. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Adaptive motion histogram moving objects segmentation Intelligent transportation system Vehicles detection

来源：评论

学校读者我要写书评

暂无评论

Unsupervised motion detection with background update and shadow suppression

引用

控制理论与应用（英文版） 2012年第4期10卷 517-522页

作者： Yepeng GUAN School of Communication and Information Engineering Shanghai University Key Laboratory of Advanced Displays and System Application Ministry of Education

An algorithm is developed to detect moving object and suppress shadow. According to motion variations caused by some moving objects in a scene, a background update approach is proposed. The developed update method ef- ficiently prevents undesired corruption of background and does not consider the adaptation coefficient or the learning rate used in some existing algorithms. A multi-scale wavelet transform methodology is used to segment foreground from a clut- ter background. The optimal selection of threshold value is automatically determined which does not require any complex supervised training or manual calibration. According to photometric invariant, a color ratio difference is proposed to sup- press shadow. Some complete foreground motion object regions are extracted by integrating moving object segmentation in the multi-scale wavelet with shadow suppression in the color ratio difference. The mentioned method is less affected by the presence of moving objects in a scene. Experimental results show that the proposed approach is efficient in detecting motion objects and suppressing shadows by comparisons.

关键词： moving objects segmentation Shadow elimination Color ratio Multi-scale wavelet transformation

来源：评论

学校读者我要写书评

暂无评论

Memorizing GMM to Handle Sharp Changes in moving Object segmentation

Memorizing GMM to Handle Sharp Changes in Moving Object Segm...

引用

2nd International Congress on Image and Signal Processing

作者： Wang, Yanjiang Suo, Peng Qi, Yujuan China Univ Petr Coll Informat & Control Engn Dongying Shandong Peoples R China

ISBN: (纸本)9781424441303

Gaussian Mixture Model (GMM) is one of the best models for modeling a background scene with gradual changes and repetitive motions. However, it fails in segmenting moving objects when the scene changes sharply. To handle this problem, a novel. background modeling algorithm Memorizing GMM is proposed, which is inspired by the way human perceive the environment. It can make the GMM remember what the scene has ever been during the learning and updating period. Experimental results show that it can help segmenting moving objects precisely when the scene changes sharply.

关键词： GMM moving objects segmentation sharp changes background model Memorizing GMM

来源：评论

学校读者我要写书评

暂无评论

Efficient Detection and Counting of moving Vehicles with Region-Level Analysis of Video Frames

Efficient Detection and Counting of Moving Vehicles with Reg...

引用

International Conference on Soft Computing for Problem Solving (SocProS 2011)

作者： Meher, Saroj K. Murty, M. N. Indian Stat Inst Syst Sci & Informat Unit Bangalore 560059 Karnataka India Indian Inst Sci Dept CSA Bangalore 560012 Karnataka India

ISBN: (纸本)9788132204909

The present article discusses the problem of detecting and counting of moving vehicle (MV) in a road traffic scenario, where background subtraction (BS) plays a vital role. BS in a video sequence is an open problem with many practical applications including camera surveillance system, human-computer interactions, etc. Among the various methods of BS, frame difference method is a simple and most adopted one. However, the performance of frame deference method depends on the proper selection of a set of frames. To meet this problem, we describe an efficient and fast processing approach for detecting and counting of MVs. A region/block-level analysis of frames is performed in this approach which requires less processing time and provides more accurate results compared to the conventional pixel-level analysis. We have used fuzzy flood fill mean shift based segmentation algorithm for this present study, which is robust under the illumination effects;such as shadows, shades, and highlights. In pixel-level analysis, segmentation operation is performed on the difference frame obtained from two test frames and detection of MV is made subsequently. However, detection with region-level analysis is made based on the geometric movement of regions between frames. Performance comparison between these two methods is made and superiority of the region-level based analysis is validated through examples. Also, we describe the estimation of the number of vehicles in a multiple MV traffic scenarios.

关键词： Intelligent transport system vehicle tracking moving objects segmentation vehicles detection fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

USING LIFTING WAVELET TO SEGMENT moving objects FROM VIDEOS

USING LIFTING WAVELET TO SEGMENT MOVING OBJECTS FROM VIDEOS

引用

6th International Conference on Wavelet Analysis and Pattern Recognition

作者： Dai, Liu-Ling Gu, Fang-Fang Liu, Wan-Chun Zhu, Lie-Huang Beijing Inst Technol Sch Comp Sci & Technol Beijing Lab Intelligent Informat Technol Beijing 100081 Peoples R China

ISBN: (纸本)9781424422388

In this paper, we present an algorithm for segmenting moving objects from videos based on lifting wavelet transform. We firstly segment a video into scenes. Then several iteration of I D lifting wavelet transforms are performed on each scene on time axis. Because wavelets have the ability of telling apart different frequency, we can extract the sections with high frequency, which represent the moving objects in a video. At last, through binarization and morphologic processing, the moving objects are completely segmented out. Experiments on videos show that our algorithm is very fast and feasible.

关键词： moving objects segmentation Lifting wavelet Image processing Video processing

来源：评论

学校读者我要写书评

暂无评论

Unsupervised moving object segmentation using background subtraction and optimal adversarial noise sample search

引用

PATTERN RECOGNITION 2022年 129卷

作者： Sultana, Maryam Mahmood, Arif Jung, Soon Ki Mohamed Bin Zayed Univ AI Dept Comp Vis Abu Dhabi U Arab Emirates Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea Informat Technol Univ ITU Dept Comp Sci Lahore Pakistan

moving objects segmentation (MOS) is a fundamental task in many computer vision applications such as human activity analysis, visual object tracking, content based video search, traffic monitoring, surveillance, and security. MOS becomes challenging due to abrupt illumination variations, dynamic backgrounds, camouflage and scenes with bootstrapping. To address these challenges we propose a MOS algorithm exploiting multiple adversarial regularizations including conventional as well as least squares losses. More specifically, our model is trained on scene background images with the help of cross-entropy loss, least squares adversarial loss and l(1) loss in image space working jointly to learn the dynamic background changes. During testing, our proposed method aims to generate test image background scenes by searching optimal noise samples using joint minimization of l(1) loss in image space, l(1) loss in feature space, and discriminator least squares loss. These loss functions force the generator to synthesize dynamic backgrounds similar to the test sequences which upon subtraction results in moving objects segmentation. Experimental evaluations on five benchmark datasets have shown excellent performance of the proposed algorithm compared to the twenty one existing state-of-the-art methods. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： moving objects segmentation Generative adversarial network Background subtraction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：