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Hole filling using joint bilateral filtering for moving object segmentation

JOURNAL OF ELECTRONIC IMAGING 2014年第6期23卷 1-17页

作者： Liu, Ran Li, Bole Huang, Zhengwei Cao, Donghua Tan, Yingchun Deng, Zekun Xu, Miao Jia, Ruishuang Tan, Weimin Chongqing Univ Coll Commun Engn Chongqing 400044 Peoples R China Chongqing Key Lab Software Theory & Technol Chongqing 400044 Peoples R China Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China

Due to various reasons (e.g., poor quality of image, sudden changes in light conditions, occlusions, or objects with similar colors), holes may appear in the moving object region when performing moving object segmentation. How to remove these holes is a crucial problem in high-quality segmentation. A moving object segmentation approach combining symmetrical differencing and joint bilateral filtering is proposed. One important aspect of our approach is that joint bilateral filtering is used to eliminate/reduce the holes in the moving object region, which makes the extracted moving object more accurate and complete, in many cases in contrast to the reference temporal difference approaches. The approach first performs block-based symmetrical differencing to obtain coarse moving object regions. Then, a joint bilateral filter that uses the current gray image as a guide image is adopted to smooth the difference image. Next, edge detection is performed on the smoothed difference image and the current gray image, respectively, to get the edges of moving objects. Finally, the undesired background is distinguished from the moving object and is cut off by a postprocessing module. In addition, the fast joint bilateral filters that can be used as substitutes for the classical joint bilateral filter are discussed. Experimental results show that this approach can effectively fill the holes in the moving object region and provide an improvement in accuracy of moving object segmentation in many cases. (C) 2014 SPIE and IS&T

关键词： video segmentation moving object segmentation joint bilateral filtering symmetrical differencing

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Learning Temporal Distribution and Spatial Correlation Toward Universal moving object segmentation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 2447-2461页

作者： Dong, Guanfang Zhao, Chenqiu Pan, Xichen Basu, Anup Univ Alberta Dept Comp Sci Edmonton AB T6G2S4 Canada Univ British Columbia Vancouver BC V6T1Z4 Canada

The goal of moving object segmentation is separating moving objects from stationary backgrounds in videos. One major challenge in this problem is how to develop a universal model for videos from various natural scenes since previous methods are often effective only in specific scenes. In this paper, we propose a method called Learning Temporal Distribution and Spatial Correlation (LTS) that has the potential to be a general solution for universal moving object segmentation. In the proposed approach, the distribution from temporal pixels is first learned by our Defect Iterative Distribution Learning (DIDL) network for a scene-independent segmentation. Notably, the DIDL network incorporates the use of an improved product distribution layer that we have newly derived. Then, the Stochastic Bayesian Refinement (SBR) Network, which learns the spatial correlation, is proposed to improve the binary mask generated by the DIDL network. Benefiting from the scene independence of the temporal distribution and the accuracy improvement resulting from the spatial correlation, the proposed approach performs well for almost all videos from diverse and complex natural scenes with fixed parameters. Comprehensive experiments on standard datasets including LASIESTA, CDNet2014, BMC, SBMI2015 and 128 real world videos demonstrate the superiority of proposed approach compared to state-of-the-art methods with or without the use of deep learning networks. To the best of our knowledge, this work has high potential to be a general solution for moving object segmentation in real world environments. The code and real-world videos can be found on GitHub https://***/guanfangdong/LTS-UniverisalMOS.

关键词： moving object segmentation distribution learning Bayesian probabilistic model machine learning

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A moving object segmentation algorithm for static camera via active contours and GMM

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Science in China(Series F) 2009年第2期52卷 322-328页

作者： WAN ChengKai YUAN BaoZong MIAO ZhenJiang Institute of Information Science Beijing Jiaotong University Beijing 100044 China

moving object segmentation is one of the most challenging issues in computer vision. In this paper, we propose a new algorithm for static camera foreground segmentation. It combines Gaussian mixture model （GMM） and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Our algorithm integrates the GMM background model, shadow elimination term and curve evolution edge stopping term into energy function. It achieves more accurate segmentation than existing methods of the same type. Promising results on real images demonstrate the potential of the presented method.

关键词： moving object segmentation active contours GMM, level set

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Fast moving object segmentation Based On Active Contours

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JOURNAL OF COMPUTERS 2012年第4期7卷 863-869页

作者： Zhang, Hui-min Duan, Zhi-yong Zhu, Zhong-jie Wang, Yuer Zhengzhou Univ Phys Engn Coll Zhengzhou 450001 Henan Peoples R China Zhejiang Wanli Univ Ningbo Key Lab DSP Ningbo 315100 Zhejiang Peoples R China

Active contour method is widely used in the image processing field. Recently, it has been used in object segmentation and has attracted great attention. However, most of the existing object segmentation methods based on active contours are complex and time-consuming. They cannot be used in some real-time applications. Hence, in this paper, a fast and efficient moving object segmentation algorithm based on an improved active contour model is proposed. Firstly, moving regions are detected based on frame differences. Then their boundaries are extracted and used as the coarse objects' contours for the next segmentation based on active contour model. Thirdly, an improved active contour method is introduced to extract the final objects with the coarse objects' contours as initial values. Experiments are implemented and the results show that the proposed algorithm can segment and track moving targets quickly and correctly.

关键词： moving object segmentation frame difference active contour model fast algorithm

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Particle Swarm Clustering-based moving object segmentation in the H.264 Compressed Domain

Particle Swarm Clustering-based Moving Object Segmentation i...

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International Conference on Materials Science and Information Technology (MSIT 2011)

作者： Wang Pei Wang Jing Shanghai Normal Univ Shanghai Peoples R China

ISBN: (纸本)9783037853191

An approach of the moving object segmentation is proposed in this paper. Firstly the motion fields are extracted from the compressed stream, where the noise and the unreal motion blocks are removed by vector median filter. Then the motion vectors are accumulated by motion estimation, in order to get denser and prominent motion vectors. Finally the moving objects are segmented adaptively by particle swarm clustering algorithm. It is demonstrated by the experimental results that the moving objects in the compressed domain can be segmented effectively.

关键词： particle swarm clustering compressed domain moving object segmentation

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A Framework for moving object segmentation under Rapidly Changing Illumination Conditions in Complex Wavelet Domain 1

A Framework for Moving Object Segmentation under Rapidly Cha...

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International Conference On Futuristic Trends in Computational Analysis and Knowledge Management

作者： Kushwaha, Alok Kumar Singh Srivastava, Rajeev Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

ISBN: (纸本)9781479984336

moving object segmentation using change detection in wavelet domain under dynamic background changes is a challenging problem in video surveillance system. There are several literature surveys available in change detection using wavelet domain for moving object segmentation but most of the research work are based on static background changes. Change detection under background changes is a challenging task and it has not been addressed in effectively in literature. To address this issues, a fast and robust moving object segmentation approach is proposed in dynamic background changes which consist of six steps applied on given video frames which include: wavelet decomposition of frames using complex wavelet transform;use of change detection on detail coefficients (LH, HL, HH);use of background modeling on approximate co-efficient (LL sub-band);strong edge detection;inverse wavelet transformation for reconstruction;and finally using closing morphology operator. For dynamic background modeling, we have improved the Gaussian mixture model and use mode value to find the variance of K-Gaussian. A comparative analysis of the proposed method is presented both quantitatively and qualitatively with other standard methods available in the literature. The various performance measure used for quantitative analysis include RFAM, RPM, NCC and MP. From the obtained result, it is observed that proposed approach is performing better in comparison to other methods in consideration.

关键词： Video surveillance Background modeling moving object segmentation Change detection

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INDUCTIVE GRAPH NEURAL NETWORKS FOR moving object segmentation 30

INDUCTIVE GRAPH NEURAL NETWORKS FOR MOVING OBJECT SEGMENTATI...

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30th IEEE International Conference on Image Processing (ICIP)

作者： Prummel, Wieke Giraldo, Jhony H. Zakharova, Anastasia Bouwmans, Thierry La Rochelle Univ Lab Math Image & Applicat MIA La Rochelle France Inst Polytech Paris Telecom Paris LTCI Paris France

ISBN: (纸本)9781728198354

moving object segmentation (MOS) is a challenging problem in computer vision, particularly in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and moving cameras. While graph-based methods have shown promising results in MOS, they have mainly relied on transductive learning which assumes access to the entire training and testing data for evaluation. However, this assumption is not realistic in real-world applications where the system needs to handle new data during deployment. In this paper, we propose a novel Graph Inductive moving object segmentation (GraphIMOS) algorithm based on a Graph Neural Network (GNN) architecture. Our approach builds a generic model capable of performing prediction on newly added data frames using the already trained model. GraphIMOS outperforms previous inductive learning methods and is more generic than previous transductive techniques. Our proposed algorithm enables the deployment of graph-based MOS models in real-world applications.

关键词： moving object segmentation graph neural networks inductive learning graph signal processing

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The Emerging Field of Graph Signal Processing for moving object segmentation 27th

The Emerging Field of Graph Signal Processing for Moving Obj...

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International Workshop on Frontiers of Computer Vision (IW-FCV)

作者： Giraldo, Jhony H. Javed, Sajid Sultana, Maryam Jung, Soon Ki Bouwmans, Thierry La Rochelle Univ F-17000 La Rochelle France Khalifa Univ Abu Dhabi 127788 U Arab Emirates Kyungpook Natl Univ Daegu 41566 South Korea

ISBN: (纸本)9783030816384;9783030816377

moving object segmentation (MOS) is an important topic in computer vision. MOS becomes a challenging problem in the presence of dynamic background and moving camera videos such as Pan-Tilt-Zoom cameras (PTZ). The MOS problem has been solved using unsupervised and supervised learning strategies. Recently, new ideas to solve MOS using semi-supervised learning have emerged inspired from the theory of Graph Signal Processing (GSP). These new algorithms are usually composed of several steps including: segmentation, background initialization, features extraction, graph construction, graph signal sampling, and a semi-supervised learning algorithm inspired from reconstruction of graph signals. In this work, we summarize and explain the theoretical foundations as well as the technical details of MOS using GPS. We also propose two architectures for MOS using semi-supervised learning and a new evaluation procedure for GSP-based MOS algorithms. GSP-based algorithms are evaluated in the Change Detection (CDNet2014) dataset for MOS, outperforming numerous State-Of-The-Art (SOTA) methods in several challenging conditions.

关键词： moving object segmentation Graph Signal Processing Semi-supervised learning

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Towards Optical Flow Ego-motion Compensation for moving object segmentation

Towards Optical Flow Ego-motion Compensation for Moving Obje...

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International Conference on Robotics, Computer Vision and Intelligent Systems (ROBOVIS)

作者： Elek, Renata Nagyne Karoly, Artur, I Haidegger, Tunas Galambos, Peter Obuda Univ Univ Res & Innovat Ctr Antal Bejczy Ctr Intelligent Robot Budapest Hungary Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary Obuda Univ John von Neumann Fac Informat Budapest Hungary

ISBN: (纸本)9789897584794

Optical flow is an established tool for motion detection in the visual scene. While optical flow algorithms usually provide accurate results, they can not make a difference between image-space displacements originated from moving objects in the space and the ego-motion of the moving viewpoint. In the case of optical flow-based moving object segmentation, camera ego-motion compensation is essential. Hereby, we show the preliminary results of a moving viewpoint optical flow ego-motion filtering method, using two dimensional optical flow, image depth information and the camera holder robot arm's state of motion. We tested its accuracy through physical experiments, where the camera was fixed on a robot arm, and a test object was attached onto an other robot arm. The test object and the camera were moved relative to each other along given trajectories in different scenarios. We validated our method for optical flow background filtering, which showed 94.88% mean accuracy in the different test cases. Furthermore, we tested the proposed algorithm for moving object state of motion estimation, which showed high accuracy in the case of translational and rotational movements without depth variation, but lower accuracy, when the relative motion produced change in depth, or the camera and the moving object move in the same directions. The proposed method with future work including outlier filtering and optimisation could become useful in various robot navigation applications and optical flow-based computer vision problems.

关键词： Optical Flow Ego-motion Velocity Compensation moving object segmentation Robotics

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Towards Dense moving object segmentation based Robust Dense RGB-D SLAM in Dynamic Scenarios 13

Towards Dense Moving Object Segmentation based Robust Dense ...

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13th International Conference on Control Automation Robotics & Vision (ICARCV)

作者： Wang, Youbing Huang, Shoudong Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia

ISBN: (纸本)9781479951994

Based on the latest achievements in computer vision and RGB-D SLAM, a practical way for dense moving object segmentation and thus a new framework for robust dense RGB-D SLAM in challenging dynamic scenarios is put forward. As the state-of-the-art method in RGB-D SLAM, dense SLAM is very robust when there are motion blur or featureless regions, while most of those sparse feature-based methods could not handle them. However, it is very susceptible to dynamic elements in the scenarios. To enhance its robustness in dynamic scenarios, we propose to combine dense moving object segmentation with dense SLAM. Since the object segmentation results from the latest available algorithm in computer vision are not satisfactory, we propose some effective measures to improve upon them so that better results can be achieved. After dense segmentation of dynamic objects, dense SLAM can be employed to estimate the camera poses. Quantitative results from the available challenging benchmark dataset have proved the effectiveness of our method.

关键词： moving object segmentation motion segmentation robustness RGB-D SLAM

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