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Semantic-Independent Dynamic SLAM Based on Geometric Re-Clustering and Optical Flow Residuals

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第3期35卷 2244-2259页

作者： Qi, Hengbo Chen, Xuechao Yu, Zhangguo Li, Chao Shi, Yongliang Zhao, Qingrui Huang, Qiang Beijing Inst Technol BIT Sch Mechatron Engn Beijing 100081 Peoples R China Qiyuan Lab Beijing 100085 Peoples R China

Dynamic objects pose significant challenges to the accuracy of state estimation and map quality in Simultaneous Localization and Mapping (SLAM). While current dynamic SLAM methods often rely on semantic information to detect specific movable objects, this dependency on pre-trained models and semantic priors can lead to false dynamic detections. This paper presents a novel semantic-independent dynamic SLAM method that detects truly moving regions, without being constrained by the classes or motion patterns of dynamic objects. We introduce a geometric re-clustering approach to improve object clustering by addressing the under- and over-segmentation caused by the K-Means algorithm. Next, instead of simply classifying entire clusters as dynamic or static, we propose a method to detect dynamic regions within each cluster based on dense optical flow residuals. This enables the detection of partial object movements, such as a seated person moving only his hands. Dynamic detection results are propagated across consecutive frames as dynamic priors for calculating optical flow residuals. Additionally, to enhance map quality, we address the mis-detection of slowly or intermittently moving objects through depth consistency checks applied over a larger time interval. Extensive evaluations on public datasets (TUM and Bonn) and real-world scenes show that our method outperforms state-of-the-art semantic-based methods in terms of localization accuracy and generalizability across various scenarios, particularly when facing unknown dynamic objects. Our method also achieves clean and dense reconstructions, demonstrating its potential for applications like robot navigation in dynamic environments.

关键词： Dynamic SLAM dynamic region detection moving object segmentation moving object segmentation dense construction dense construction semantic- independent semantic- independent

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An Improved segmentation Algorithm to Detect moving object in Video Sequences

An Improved Segmentation Algorithm to Detect Moving Object i...

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Conference on Optoelectronic Imaging and Multimedia Technology

作者： Li, Jinkui Sang, Xinzhu Wang, Yongqiang Yan, Binbin Yu, Chongxiu Beijing Univ Posts & Telecommun Minist Educ Key Lab Informat Photon & Opt Commun Post Box 72 BUPT Beijing 100876 Peoples R China Minist Publ Secur Inst Forens Sci Beijing 100038 Peoples R China

ISBN: (纸本)9780819483805

The segmentation of moving object in video sequences is attracting more and more attention because of its important role in various camera video applications, such as video surveillance, traffic monitoring, people tracking. and so on. Conventional segmentation algorithms can be divided into two classes. One class is based on spatial homogeneity, which results in the promising output. However, the computation is too complex and heavy to be unsuitable to real-time applications. The other class utilizes change detection as the segmentation standard to extract the moving object. Typical approaches include frame difference, background subtraction and optical flow. A novel algorithm based on adaptive symmetrical difference and background subtraction is proposed. Firstly, the moving object mask is detected through the adaptive symmetrical difference, and the contour of the mask is extracted. And then, the adaptive background subtraction is carried out in the acquired region to extract the accurate moving object. Morphological operation and shadow cancellation are adopted to refine the result. Experimental results show that the algorithm is robust and effective in improving the segmentation accuracy.

关键词： moving object segmentation Symmetrical Difference Background Subtraction Adaptive Threshold

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Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Alawode, Basit Javed, Sajid Khalifa Univ Sci & Technol Dept Comp Sci Abu Dhabi U Arab Emirates

Background subtraction in videos is a core challenge in computer vision, aiming to accurately identify moving objects. Robust principal component analysis (RPCA) has emerged as a promising unsupervised (US) paradigm for this task, showing strong performance on various benchmark datasets. Building on RPCA, tensor RPCA (TRPCA) variants have further enhanced background subtraction performance. However, current TRPCA methods often treat moving object pixels independently, lacking spatial-temporal structured-sparsity constraints. This limitation leads to performance degradation in scenarios with dynamic backgrounds, camouflage, and camera jitter. In this work, we introduce a novel spatial-temporal regularized tensor sparse RPCA algorithm to address these issues. By incorporating normalized graph-Laplacian matrices into the sparse component, we enforce spatial-temporal regularization. We construct two graphs-one across spatial locations and another across temporal slices-to guide regularization. By maximizing our objective function, we ensure that the tensor sparse component aligns with the spatiotemporal eigenvectors of the graph-Laplacian matrices, preserving disconnected moving object pixels. We formulate a new objective function and employ batch and online-based optimization methods to jointly optimize background-foreground separation and spatial-temporal regularization. Experimental evaluation on six publicly available datasets demonstrates the superior performance of our algorithm compared to existing methods.

关键词： Tensors Sparse matrices Principal component analysis Computational modeling Optimization Linear programming Learning systems Videos Real-time systems object segmentation Background modeling background subtraction moving object segmentation robust principal component analysis (RPCA) structured sparsity

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Maritime object segmentation Using Dynamic Background Modeling and Shadow Suppression

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COMPUTER JOURNAL 2016年第9期59卷 1303-1329页

作者： Kushwaha, Alok Kumar Singh Srivastava, Rajeev Banaras Hindu Univ Indian Inst Technol Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India

moving object segmentation in maritime domain is a challenging task due to the various real time practical problems, such as waves on the water surface, boat wakes and weather issues (such as bright sun, fog and heavy rain). These problems contribute to generate a highly dynamic background, gradual and sudden illumination changes, camera jitter, shadows and reflections that can provoke false detections. To deal with above issues, in this paper a fast and robust moving object segmentation method on a water surface for maritime surveillance using dynamic background modeling and shadow suppression (OSDBMSS) in the complex wavelet domain is proposed. For dynamic background modeling, we have used frame difference, background registration, background difference and background difference mask in the complex wavelet domain. For shadow detection and removal, we exploit the high frequency subband in the complex wavelet domain. A comparative analysis of the proposed method is presented both qualitatively and quantitatively with other standard methods available in the literature for publicly available datasets of videos in different maritime scenarios, with varying light and weather conditions. Experimental results indicate that the proposed method is performing better in comparison to other standard methods for all the test cases.

关键词： maritime video surveillance moving object segmentation dynamic background modeling change and shadow detection complex wavelet domain

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New fuzzy object segmentation algorithm for video sequences

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2008年第2期24卷 521-537页

作者： Chung, Kuo-Liang Yu, Shifi-Wei Yeh, Hsueh-Ju Huang, Yong-Huai Yao, Ta-Jen Natl Taiwan Univ Sci & Tachnol Dept Comp Sci & Informat Engn Taipei 106 Taiwan

A new fuzzy moving object segmentation algorithm for video sequences is presented in this paper. Our proposed efficient object segmentation algorithm consists of three steps, namely the spatial segmentation step, the temporal tracking step, and the step for identifying the moving object from the frame in a fuzzy way. Especially, our proposed algorithm can robustly distinguish the foreground part, which is a near stationary region but Surrounded by some regions with moving variation, from the background. Using several different real video sequences, experimental results demonstrate that the object segmentation accuracy of our proposed fuzzy-based algorithm is encouraging when compared to the recently published object segmentation algorithms by Chien et al. and Kim et al.

关键词： fuzzy approach moving object segmentation MPEG-4 spatial segmentation temporal tracking

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A new user-assisted segmentation and tracking technique for object-based video editing system

A new user-assisted segmentation and tracking technique for ...

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Conference on Microelectronics - Design, Technology and Packaging

作者： Yu, HY Hong, SH Lee, MMO Choi, JG Chonnam Natl Univ Dept Elect Comp & Informat Eng Kwangju South Korea

ISBN: (纸本)0819451673

This paper presents a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the user-guided and selected objects are continuously separated from the unselected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable and efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on these results, we have developed objects based video editing system with several convenient editing functions.

关键词： moving object segmentation semi-automatic segmentation user-assistance visual object tracking video editing motion vector field morphological watershed video object composition

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A framework for unsupervised mesh based segmentation of moving objects

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MULTIMEDIA TOOLS AND APPLICATIONS 2010年第1期50卷 7-28页

作者： Kriechbaum, Andreas Moerzinger, Roland Thallinger, Georg JOANNEUM RES Inst Informat Syst A-8010 Graz Austria

Multimedia analysis usually deals with a large amount of video data with a significant number of moving objects. Often it is necessary to reduce the amount of data and to represent the video in terms of moving objects and events. Event analysis can be built on the detection of moving objects. In order to automatically process a variety of video content in different domain, largely unsupervised moving object segmentation algorithms are needed. We propose a fully unsupervised system for moving object segmentation that does not require any restriction on the video content. Our approach to extract moving objects relies on a mesh-based combination of results from colour segmentation (Mean Shift) and motion segmentation by feature point tracking (KLT tracker). The proposed algorithm has been evaluated using precision and recall measures for comparing moving objects and their colour segmented regions with manually labelled ground truth data. Results show that the algorithm is comparable to other state-of-the-art algorithms. The extracted information is used in a search and retrieval tool. For that purpose a moving object representation in MPEG-7 is implemented. It facilitates high performance indexing and retrieval of moving objects and events in large video databases, such as the search for similar moving objects occurring in a certain period.

关键词： moving object segmentation Unsupervised system Spatial segmentation Motion extraction Clustering Optical flow

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One-against-all frame differences based hand detection for human and mobile interaction

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NEUROCOMPUTING 2013年 120卷 185-191页

作者： Wang, Kongqiao Xu, Lei Fang, Yikai Li, Jiangwei Nokia Res Ctr Beijing 100176 Peoples R China

Hand gesture based human-computer interaction with mobile phones heavily relies on robust hand detection. Because of the limited computational resources of mobile phones, computationally complex algorithms are infeasible. In this paper, we discover that: though traditional frame difference based methods are computationally inexpensive, they have the following problems in many situations: (1) may completely lose the target (we call this as "the phenomenon blind problem");(2) can detect partial or delayed version of the target (we call this as "the phenomenon delay problem");and (3) detect the object with many false-alarms (we call this as "the false-alarm problem"). To deal with these problems, we propose an effective and efficient frame-difference algorithm. The proposed algorithm computes one-against-all frame differences and combines them using intersection operators. To determine which pixels in the frame difference are object pixels in interest, we develop a coarse-to-fine binarization procedure. The Hamming distance between LBP (Local Binary Patterns) feature and a local adaptive thresholding scheme are employed in the coarse and fine stages respectively. Experimental results demonstrate the effectiveness and efficiency of the proposed method. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Human-computer interface Hand detection Frame difference moving object segmentation One-against-all

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Modeling background and segmenting moving objects from compressed video

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2008年第5期18卷 670-681页

作者： Wang, Weiqiang Yang, Jie Gao, Wen Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China Chinese Acad Sci Grad Sch Beijing 100080 Peoples R China Carnegie Mellon Univ Human Comp Interact Inst Pittsburgh PA 15213 USA Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

Modeling background and segmenting moving objects are significant techniques for video surveillance and other video processing applications. Most existing methods of modeling background and segmenting moving objects mainly operate in the spatial domain at pixel level. In this paper, we present three new algorithms (running average, median, mixture of Gaussians) modeling background directly from compressed video, and a two-stage segmentation approach based on the proposed background models. The proposed methods utilize discrete cosine transform (DCT) coefficients (including ac coefficients) at block level to represent background, and adapt the background by updating DCT coefficients. The proposed segmentation approach can extract foreground objects with pixel accuracy through a two-stage process. First a new background subtraction technique in the DCT domain is exploited to identify the block regions fully or partially occupied by foreground objects, and then pixels from these foreground blocks are further classified in the spatial domain. The experimental results show the proposed background modeling algorithms can achieve comparable accuracy to their counterparts in the spatial domain, and the associated segmentation scheme can visually generate good segmentation results with efficient computation. For instance, the computational cost of the proposed median and MoG algorithms are only 40.4% and 20.6 % of their counterparts in the spatial domain for background construction.

关键词： background models discrete cosine transform (DCT) moving object segmentation video surveillance

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Unsupervised Tracking, Roughness and Quantitative Indices

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FUNDAMENTA INFORMATICAE 2013年第1-2期124卷 63-90页

作者： Pal, Sankar K. Chakraborty, Debarati Indian Stat Inst Ctr Soft Comp Res Kolkata 700108 India

This paper presents a novel methodology for tracking a single moving object in a video sequence applying the concept of rough set theory. The novelty of this technique is that it does not consider any prior information about the video sequence unlike many existing techniques. The first target model is constructed using the median filtering based foreground detection technique and after that the target is reconstructed in every frame according to the rough set based feature reduction concept incorporating a measure of indiscernibility instead of indiscernibility matrix. The area of interest is initially defined roughly in every frame based on the object shift in the previous frames, and after reduction of redundant features the object is tracked. The measure of indiscernibility of a feature is defined based on its degree of belonging (DoB) to the target. Three quantitative indices based on rough sets, feature similarity and Bhattacharya distance are proposed to evaluate the performance of tracking and detect the mis-tracked frames in the process of tracking to make those corrected. Unlike many existing measures, the proposed ones do not require to know the ground truth or trajectory of the video sequence. Extensive experimental results are given to demonstrate the effectiveness of the method. Comparative performance is demonstrated both visually and quantitatively.

关键词： Rough Set Unsupervised Tracking Feature Reduction Bhattacharya distance moving object segmentation

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