• Title/Summary/Keyword: Video object segmentation

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Object segmentation and object-based surveillance video indexing

  • Kim, Jin-Woong;Kim, Mun-Churl;Lee, Kyu-Won;Kim, Jae-Gon;Ahn, Chie-Teuk
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 1999.06a
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    • pp.165.1-170
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    • 1999
  • Object segmentation fro natural video scenes has recently become one of very active research to pics due to the object-based video coding standard MPEG-4. Object detection and isolation is also useful for object-based indexing and search of video content, which is a goal of the emerging new standard, MPEG-7. In this paper, an automatic segmentation method of moving objects in image sequence is presented which is applicable to multimedia content authoring for MPEG-4, and two different segmentation approaches suitable for surveillance applications are addressed in raw data domain and compressed bitstream domains. We also propose an object-based video description scheme based on object segmentation for video indexing purposes.

Implementation of Video Object Segmentation System for Interactive Personal Broadcasting Service (양방향 개인방송 서비스를 위한 동영상 객체분할 시스템의 구현)

  • Yu, Hong-Yeon;Jun, Do-Young;Kim, Min-Sung;Hong, Sung-Hoon
    • Proceedings of the KIEE Conference
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    • 2007.04a
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    • pp.17-19
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    • 2007
  • This paper describe an interactive video object segmentation tool which can be used to generate MPEG-4 video object planes for multimedia broadcasting and enables content based functionalities. In order to apply these functionalities, each frame of video sequence should be represented in terms of video objects. Semiautomatic segmentation can be thought of as a user-assisted segmentation technique. A user can initially mark objects of interest around the real object boundaries. Then the user-guided and selected objects are continuously separated from the unselected areas though time evolution in the image sequences. We proposed method shows very promising result and this encourages the development of object based video editing system.

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Video Object Segmentation with Weakly Temporal Information

  • Zhang, Yikun;Yao, Rui;Jiang, Qingnan;Zhang, Changbin;Wang, Shi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.3
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    • pp.1434-1449
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    • 2019
  • Video object segmentation is a significant task in computer vision, but its performance is not very satisfactory. A method of video object segmentation using weakly temporal information is presented in this paper. Motivated by the phenomenon in reality that the motion of the object is a continuous and smooth process and the appearance of the object does not change much between adjacent frames in the video sequences, we use a feed-forward architecture with motion estimation to predict the mask of the current frame. We extend an additional mask channel for the previous frame segmentation result. The mask of the previous frame is treated as the input of the expanded channel after processing, and then we extract the temporal feature of the object and fuse it with other feature maps to generate the final mask. In addition, we introduce multi-mask guidance to improve the stability of the model. Moreover, we enhance segmentation performance by further training with the masks already obtained. Experiments show that our method achieves competitive results on DAVIS-2016 on single object segmentation compared to some state-of-the-art algorithms.

Uncertain Region Based User-Assisted Segmentation Technique for Object-Based Video Editing System (객체기반 비디오 편집 시스템을 위한 불확실 영역기반 사용자 지원 비디오 객체 분할 기법)

  • Yu Hong-Yeon;Hong Sung-Hoon
    • Journal of Korea Multimedia Society
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    • v.9 no.5
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    • pp.529-541
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    • 2006
  • In this paper, we propose 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 selected objects are continuously separated from the un selected 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 this result, we have developed objects based video editing system with several convenient editing functions.

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Higher-Order Conditional Random Field established with CNNs for Video Object Segmentation

  • Hao, Chuanyan;Wang, Yuqi;Jiang, Bo;Liu, Sijiang;Yang, Zhi-Xin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.9
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    • pp.3204-3220
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    • 2021
  • We perform the task of video object segmentation by incorporating a conditional random field (CRF) and convolutional neural networks (CNNs). Most methods employ a CRF to refine a coarse output from fully convolutional networks. Others treat the inference process of the CRF as a recurrent neural network and then combine CNNs and the CRF into an end-to-end model for video object segmentation. In contrast to these methods, we propose a novel higher-order CRF model to solve the problem of video object segmentation. Specifically, we use CNNs to establish a higher-order dependence among pixels, and this dependence can provide critical global information for a segmentation model to enhance the global consistency of segmentation. In general, the optimization of the higher-order energy is extremely difficult. To make the problem tractable, we decompose the higher-order energy into two parts by utilizing auxiliary variables and then solve it by using an iterative process. We conduct quantitative and qualitative analyses on multiple datasets, and the proposed method achieves competitive results.

Video Segmentation Using New Combined Measure (새로운 결합척도를 이용한 동영상 분할)

  • 최재각;이시웅;남재열
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.40 no.1
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    • pp.51-62
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    • 2003
  • A new video segmentation algorithm for segmentation-based video coding is proposed. The method uses a new criterion based on similarities in both motion and brightness. Brightness and motion information are incorporated in a single segmentation procedure. The actual segmentation is accomplished using a region-growing technique based on the watershed algorithm. In addition, a tracking technique is used in subsequent frames to achieve a coherent segmentation through time. Simulation results show that the proposed method is effective in determining object boundaries not easily found using the statistic criterion alone.

A Robust Algorithm for Moving Object Segmentation and VOP Extraction in Video Sequences (비디오 시퀸스에서 움직임 객체 분할과 VOP 추출을 위한 강력한 알고리즘)

  • Kim, Jun-Ki;Lee, Ho-Suk
    • Journal of KIISE:Computing Practices and Letters
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    • v.8 no.4
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    • pp.430-441
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    • 2002
  • Video object segmentation is an important component for object-based video coding scheme such as MPEG-4. In this paper, a robust algorithm for segmentation of moving objects in video sequences and VOP(Video Object Planes) extraction is presented. The points of this paper are detection, of an accurate object boundary by associating moving object edge with spatial object edge and generation of VOP. The algorithm begins with the difference between two successive frames. And after extracting difference image, the accurate moving object edge is produced by using the Canny algorithm and morphological operation. To enhance extracting performance, we app]y the morphological operation to extract more accurate VOP. To be specific, we apply morphological erosion operation to detect only accurate object edges. And moving object edges between two images are generated by adjusting the size of the edges. This paper presents a robust algorithm implementation for fast moving object detection by extracting accurate object boundaries in video sequences.

Automatic Object Segmentation and Background Composition for Interactive Video Communications over Mobile Phones

  • Kim, Daehee;Oh, Jahwan;Jeon, Jieun;Lee, Junghyun
    • IEIE Transactions on Smart Processing and Computing
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    • v.1 no.3
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    • pp.125-132
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    • 2012
  • This paper proposes an automatic object segmentation and background composition method for video communication over consumer mobile phones. The object regions were extracted based on the motion and color variance of the first two frames. To combine the motion and variance information, the Euclidean distance between the motion boundary pixel and the neighboring color variance edge pixels was calculated, and the nearest edge pixel was labeled to the object boundary. The labeling results were refined using the morphology for a more accurate and natural-looking boundary. The grow-cut segmentation algorithm begins in the expanded label map, where the inner and outer boundary belongs to the foreground and background, respectively. The segmented object region and a new background image stored a priori in the mobile phone was then composed. In the background composition process, the background motion was measured using the optical-flow, and the final result was synthesized by accurately locating the object region according to the motion information. This study can be considered an extended, improved version of the existing background composition algorithm by considering motion information in a video. The proposed segmentation algorithm reduces the computational complexity significantly by choosing the minimum resolution at each segmentation step. The experimental results showed that the proposed algorithm can generate a fast, accurate and natural-looking background composition.

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Video object segmentation and frame preprocessing for real-time and high compression MPEG-4 encoding (실시간 고압축 MPEG-4 부호화를 위한 비디오 객체 분할과 프레임 전처리)

  • 김준기;이호석
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.28 no.2C
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    • pp.147-161
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    • 2003
  • Video object segmentation is one of the core technologies for content-based real-time MPEG-4 encoding system. For real-time requirement, the segmentation algorithm should be fast and accurate but almost all existing algorithms are computationally intensive and not suitable for real-time applications. The MPEG-4 VM(Verification Model) has provided basic algorithms for MPEG-4 encoding but it has many limitations in practical software development, real-time camera input system and compression efficiency. In this paper, we implemented the preprocessing system for real-time camera input and VOP extraction for content-based video coding and also implemented motion detection to achieve the 180 : 1 compression rate for real-time and high compression MPEG-4 encoding.

Effective segmentation of non-rigid object in a still picture and video sequences (정지영상/동영상에서 non-rigid object의 효율적인 영역 분할 방식에 관한 연구)

  • Lee, In-Jae;Kim, Yong-Ho;Kim, Jung-Gyu;Lee, Myeong-Ho;An, Chi-Deuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.39 no.1
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    • pp.17-31
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    • 2002
  • The new MPEG-4 video coding standard enables content-based functionalities. Image segmentation is an indispensable process for it. This paper addresses an effective segmentation of non-rigid objects. Non-rigid objects are deformable objects with fuzzy, blurred and indefinite boundaries. So it is difficult to segment deformable objects precisely. In order to solve this problem, we propose an effective segmentation of non-rigid objects using watershed algorithms in still pictures. And we propose an automatic segmentation through intra-frame and inter-frame segmentation process in video sequences. Automatic segmentation preforms boundary-based and region-based segmentation to extract precise object boundaries.