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moving object segmentation in video sequences by user interaction and automatic object tracking

IMAGE AND VISION COMPUTING 2001年第5期19卷 245-260页

作者： Kim, M Jeon, JG Kwak, JS Lee, MH Ahn, C Elect & Telecommun Res Inst Radio & Broadcasting Technol Lab Broadcasting Media Technol Dept Taejon 305350 South Korea

The new MPEG-4 video coding standard consists of object based coding schemes for multimedia and enables content based functionalities. In order to apply the MPEG-4 visual standard, each frame of video sequences should be represented in terms of video object planes (VOFs). Video objects in still pictures or video sequences should be first identified before the encoding process starts. It requires a prior decomposition of sequences into VOPs so that each VOP represents an object. This paper addresses a user-assisted video segmentation method for separating moving objects from the background in video sequences. The proposed method comprises of intra-frame and inter-frame segmentation modules. The intra-frame segmentation incorporates user interaction in defining a high level semantic object of interest to be segmented and detects precise object boundary. The inter-frame segmentation involves boundary and region tracking to capture temporal coherence of moving objects with accurate object boundary information. The proposed method exhibits a consistent and faithful segmentation performance for various types of video sequences. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： moving object segmentation automatic object tracking video coding

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moving object segmentation in the H.264 Compressed Domain

Moving Object Segmentation in the H.264 Compressed Domain

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9th Asian Conference on Computer Vision

作者： Niu, Changfeng Liu, Yushu Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9783642123030

A novel method for moving object segmentation in the H.264 compressed domain is proposed. In contrast to all known methods in which only motion information is used, the proposed method utilizes some characters of H.264 besides motion information with no more decoding required. In the proposed method, motion vector is refined firstly by spatial and temporal correlation of motion and initial segmentation is produced by using the motion vector difference after global motion estimation. Then, the result of segmentation is refined by using intra prediction information in intra-frame. The refined result of segmentation is projected to subsequent frame and expansion and contraction operation is followed. Experimental results for several H.264 compressed video sequences demonstrate the good segmentation quality of the proposed approach.

关键词： moving object segmentation Compressed Domain H.264

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moving object segmentation in Surveillance Video Based on Adaptive Mixtures

Moving Object Segmentation in Surveillance Video Based on Ad...

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SICE Annual Conference

作者： Zhang, Xiaoyong Homma, Noriyasu Ichiji, Kei Abe, Makoto Sugita, Norihiro Yoshizawa, Makoto Tohoku Univ Cybersci Ctr Sendai Miyagi 980 Japan Tohoku Univ Grad Sch Engn Sendai Miyagi 980 Japan

ISBN: (纸本)9784907764432

This paper presents an adaptive mixtures-based method for segmenting moving objects in surveillance video with pixel-wise accuracy. The proposed method employs a Gaussian mixture model (GMM) to represent the intensity change of a pixel over time. The GMM consists of a background component and one or more moving object component(s). The parameters of the GMM are estimated by using an adaptive algorithm that is a non-parametric and data-driven approach. The components in the GMM are subsequently classified into a background and moving objects according to their weights in the GMM. Experimental results demonstrate that the proposed method can successfully and robustly segment the moving objects in surveillance video.

关键词： Surveillance video moving object segmentation Gaussian mixture model (GMM) adaptive mixture

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moving object segmentation using dynamic 3D graph cuts and GMM

Moving object segmentation using dynamic 3D graph cuts and G...

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8th International Conference on Signal Processing

作者： Li, Bo Yuan, Baozong Sun, Yunda Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China

ISBN: (纸本)9780780397361

It is one of the most challenging problems in computer vision how to segment moving objects accurately. In this paper, we present a novel approach to segment moving objects with edge information and temporal information using 3D Graph cuts model when cameras is fixed. moving object segmentation is modeled as finding a minimum energy of 3D graph. Our algorithm assigns n-links in 3D graph according to spatial gradient in same frame and temporal gradient in neighboring frames. Gaussian mixture model is used to assign t-links with edge difference term and shadow elimination term. Finally, a dynamic graph cuts algorithm is used to find the minimum cut of 3D graph and segments moving objects in image sequences. Experiments show that our approach achieves nice performance.

关键词： moving object segmentation 3D graph cuts GMM spatial gradient temporal gradient

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moving object segmentation using phase information

Moving object segmentation using phase information

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5th International Conference on Wavelet Analysis and Pattern Recognition

作者： Gao, Shi-Wei Guo, Lei Chen, Liang Yu, Yong Northwestern Polytech Univ Coll Automat Xian 710072 Peoples R China

ISBN: (纸本)9781424410651

In order to gel moving object in video frames, a segmentation method is proposed. The phase-based method is devise object boundary area in for detection (of moving ob the video frame. This boundary detection scheme is based on difference image the characteristics of a phase-matched difference, and based on the assessment of the phase-matched difference between image frames. It. is shown to be sensitive to even small disruptions to a consistent motion field using this segment scheme. Then, we use active contour model to find the final boundary based on the boundary area we found using phase information. The eventual results demonstrate the efficiency of the proposed moving object segmentation method.

关键词： moving object segmentation boundary detection phase detection active contour model

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Efficient moving object segmentation algorithm using background registration technique

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2002年第7期12卷 577-586页

作者： Chien, SY Ma, SY Chen, LG Natl Taiwan Univ Dept Elect Engn Taipei 106 Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei 106 Taiwan Vivotek Inc Taipei 235 Taiwan

An efficient moving object segmentation algorithm suitable for real-time content-based multimedia communication systems is proposed in this paper. First, a background registration technique is used to construct a reliable background image from the accumulated frame difference information. The moving object region is then separated from the background region by comparing the current frame with the constructed background image. Finally, a post-processing step is applied on the obtained object mask to remove noise regions and to smooth the object boundary. In situations where object shadows appear in the background region, a pre-processing gradient filter is applied on the input image to reduce the shadow effect. In order to meet the real-time requirement, no computationally intensive operation is included in this method. Moreover, the implementation is optimized using parallel processing and a processing speed of 25 QCIF fps can be achieved on a personal computer with a 450-MHz Pentium III processor. Good segmentation performance is demonstrated by the simulation results.

关键词： background registration moving object segmentation MPEG-4 video segmentation

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3D-SeqMOS: A Novel Sequential 3D moving object segmentation in Autonomous Driving

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 8782-8795页

作者： Zhuang, Yuan Li, Qipeng Chen, Yiwen Huai, Jianzhu Li, Miao Ma, Tianbing Tang, Yufei Liang, Xinlian Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430206 Peoples R China Wuhan Univ Inst Technol Sci Wuhan 430206 Peoples R China Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Anhui Peoples R China Florida Atlantic Univ Dept Comp Elect Engn & Comp Sci Boca Raton FL 33431 USA

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential moving-object-segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the existing state-of-the-art methods. We extend the proposed method to the SemanticKITTI: moving object segmentation competition and achieve the 3rd in the leaderboard, showing its effectiveness.

关键词： Point cloud compression Three-dimensional displays object segmentation Simultaneous localization and mapping Odometry Autonomous vehicles Automobiles moving object segmentation robotics vision neural network sequential point cloud LiDAR-SLAM

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A novel GPU-aware Histogram-based algorithm for supporting moving object segmentation in big-data-based IoT application scenarios

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INFORMATION SCIENCES 2019年 496卷 592-612页

作者： Cuzzocrea, Alfredo Mumolo, Enzo Univ Trieste DIA Dept Trieste Italy

Multimedia data are a popular case of Big Data that expose the classical 3V characteristics (i.e., volume, velocity and variety). Such kind of data are likely to be processed within the core layer of Internet of Things (IoT) platforms, where a multiple, typically high, number of "things" (e.g., sensors, devices, actuators, and so forth) collaborate to massively process big data for supporting intelligent algorithms running over them. In such platforms, the computational bottleneck is very often represented by the component running the main algorithm, while communication and cooperation costs still remain relevant. Inspired by this emerging trend of big-data-based IoT applications, in this paper we focus on the specific application context represented by the problem of supporting moving object segmentation over images originated in the context of big multimedia data, and we propose an innovative background maintenance approach to this end. In particular, we provide a novel GPU-aware Histogram-based moving object segmentation algorithm that adopts a pixel-oriented approach and it is based on Graphic Processing Units (GPU), called PixHMOS_GPU. PixH-MOS_GPU allows us to achieve higher performance, hence making the computational gap of big-data-based IoT applications decisively smaller. Experimental results clearly confirm our arguments. (C) 2019 Published by Elsevier Inc.

关键词： Big data management IoT application scenarios moving object segmentation GPU-aware algorithms Intelligent applications

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Graph moving object segmentation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第5期44卷 2485-2503页

作者： Giraldozuluaga, Jhony H. Javed, Sajid Bouwmans, Thierry La Rochelle Univ Lab MIA Math Image & Applicat F-17000 La Rochelle France Khalifa Univ Ctr Autonomous Robot Syst Abu Dhabi 127788 U Arab Emirates

moving object segmentation (MOS) is a fundamental task in computer vision. Due to undesirable variations in the background scene, MOS becomes very challenging for static and moving camera sequences. Several deep learning methods have been proposed for MOS with impressive performance. However, these methods show performance degradation in the presence of unseen videos;and usually, deep learning models require large amounts of data to avoid overfitting. Recently, graph learning has attracted significant attention in many computer vision applications since they provide tools to exploit the geometrical structure of data. In this work, concepts of graph signal processing are introduced for MOS. First, we propose a new algorithm that is composed of segmentation, background initialization, graph construction, unseen sampling, and a semi-supervised learning method inspired by the theory of recovery of graph signals. Second, theoretical developments are introduced, showing one bound for the sample complexity in semi-supervised learning, and two bounds for the condition number of the Sobolev norm. Our algorithm has the advantage of requiring less labeled data than deep learning methods while having competitive results on both static and moving camera videos. Our algorithm is also adapted for Video object segmentation (VOS) tasks and is evaluated on six publicly available datasets outperforming several state-of-the-art methods in challenging conditions.

关键词： Videos Task analysis Signal processing algorithms object segmentation Semisupervised learning Deep learning Complexity theory moving object segmentation graph signal processing semi-supervised learning unseen videos video object segmentation

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A systematic review of deep learning frameworks for moving object segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第8期83卷 24715-24748页

作者： Gupta, Dipika Kumar, Manish Chaudhary, Sachin Punjab Engn Coll Comp Sci Engn Sect 12 Chandigarh 160012 India Amity Univ Mohali India

moving object segmentation (MOS) in machine learning and computer vision is gaining much interest among researchers in recent times. This field of dynamic scene understanding aims to understand every pixel of a video by segmenting objects in a frame sequence from beginning till end. The goal is to generate temporally consistent and accurate pixel masks for the target object in a video. Our paper follows a systematic way to represent the available state-of-the-art literature in the field of MOS. It includes a detailed analysis of semi-supervised, unsupervised, interactive, and referring MOS. Next, various deep learning-based approaches for MOS are elaborated. In Section 4, the basic architecture of MOS has been explained with a summary of the current benchmark data sets used. Further, a summary of evaluation metrics and qualitative and quantitative results forMOS are discussed. Finally, latest available results to date are provided as well as future research directions for MOS are detailed.

关键词： moving object segmentation Deep learning Convolutional neural network(CNN) Transformers

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