• Title/Summary/Keyword: Video object segmentation

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Video Object Segmentation using Kernel Density Estimation and Spatio-temporal Coherence (커널 밀도 추정과 시공간 일치성을 이용한 동영상 객체 분할)

  • Ahn, Jae-Kyun;Kim, Chang-Su
    • Journal of IKEEE
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    • v.13 no.4
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    • pp.1-7
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    • 2009
  • A video segmentation algorithm, which can extract objects even with non-stationary backgrounds, is proposed in this work. The proposed algorithm is composed of three steps. First, we perform an initial segmentation interactively to build the probability density functions of colors per each macro block via kernel density estimation. Then, for each subsequent frame, we construct a coherence strip, which is likely to contain the object contour, by exploiting spatio-temporal correlations. Finally, we perform the segmentation by minimizing an energy function composed of color, coherence, and smoothness terms. Experimental results on various test sequences show that the proposed algorithm provides accurate segmentation results.

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Moving Object Tracking Method in Video Data Using Color Segmentation (칼라 분할 방식을 이용한 비디오 영상에서의 움직이는 물체의 검출과 추적)

  • 이재호;조수현;김회율
    • Proceedings of the IEEK Conference
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    • 2001.06d
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    • pp.219-222
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    • 2001
  • Moving objects in video data are main elements for video analysis and retrieval. In this paper, we propose a new algorithm for tracking and segmenting moving objects in color image sequences that include complex camera motion such as zoom, pan and rotating. The Proposed algorithm is based on the Mean-shift color segmentation and stochastic region matching method. For segmenting moving objects, each sequence is divided into a set of similar color regions using Mean-shift color segmentation algorithm. Each segmented region is matched to the corresponding region in the subsequent frame. The motion vector of each matched region is then estimated and these motion vectors are summed to estimate global motion. Once motion vectors are estimated for all frame of video sequences, independently moving regions can be segmented by comparing their trajectories with that of global motion. Finally, segmented regions are merged into the independently moving object by comparing the similarities of trajectories, positions and emerging period. The experimental results show that the proposed algorithm is capable of segmenting independently moving objects in the video sequences including complex camera motion.

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A Study on Color Fuzzy Decision Algorithm in Video Object Segmentation

  • Byun, Oh-Sung;Moon, Sung-Ryong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.2
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    • pp.142-148
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    • 2004
  • In this paper, we propose the color fuzzy decision algorithm to face segmentation in a color image. Our algorithm can segment without the user's interaction by fuzzy decision marking. And it removes small parts such as a noise using wavelet morphology in the image obtained by applying the fuzzy decision algorithm. Also, it merges and chooses the face region in each quantization image through rough sets. This video object division algorithm is shown to be superior to a conventional algorithm.

Computation ally Efficient Video Object Segmentation using SOM-Based Hierarchical Clustering (SOM 기반의 계층적 군집 방법을 이용한 계산 효율적 비디오 객체 분할)

  • Jung Chan-Ho;Kim Gyeong-Hwan
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.43 no.4 s.310
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    • pp.74-86
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    • 2006
  • This paper proposes a robust and computationally efficient algorithm for automatic video object segmentation. For implementing the spatio-temporal segmentation, which aims for efficient combination of the motion segmentation and the color segmentation, an SOM-based hierarchical clustering method in which the segmentation process is regarded as clustering of feature vectors is employed. As results, problems of high computational complexity which required for obtaining exact segmentation results in conventional video object segmentation methods, and the performance degradation due to noise are significantly reduced. A measure of motion vector reliability which employs MRF-based MAP estimation scheme has been introduced to minimize the influence from the motion estimation error. In addition, a noise elimination scheme based on the motion reliability histogram and a clustering validity index for automatically identifying the number of objects in the scene have been applied. A cross projection method for effective object tracking and a dynamic memory to maintain temporal coherency have been introduced as well. A set of experiments has been conducted over several video sequences to evaluate the proposed algorithm, and the efficiency in terms of computational complexity, robustness from noise, and higher segmentation accuracy of the proposed algorithm have been proved.

Segmentation of Objects of Interest for Video Content Analysis (동영상 내용 분석을 위한 관심 객체 추출)

  • Park, So-Jung;Kim, Min-Hwan
    • Journal of Korea Multimedia Society
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    • v.10 no.8
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    • pp.967-980
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    • 2007
  • Video objects of interest play an important role in representing the video content and are useful for improving the performance of video retrieval and compression. The objects of interest may be a main object in describing contents of a video shot or a core object that a video producer wants to represent in the video shot. We know that any object attracting one's eye much in the video shot may not be an object of interest and a non-moving object may be an object of interest as well as a moving one. However it is not easy to define an object of interest clearly, because procedural description of human interest is difficult. In this paper, a set of four filtering conditions for extracting moving objects of interest is suggested, which is defined by considering variation of location, size, and moving pattern of moving objects in a video shot. Non-moving objects of interest are also defined as another set of four extracting conditions that are related to saliency of color/texture, location, size, and occurrence frequency of static objects in a video shot. On a test with 50 video shots, the segmentation method based on the two sets of conditions could extract the moving and non-moving objects of interest chosen manually on accuracy of 84%.

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A Fast Semiautomatic Video Object Tracking Algorithm (고속의 세미오토매틱 비디오객체 추적 알고리즘)

  • Lee, Jong-Won;Kim, Jin-Sang;Cho, Won-Kyung
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.291-294
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    • 2004
  • Semantic video object extraction is important for tracking meaningful objects in video and object-based video coding. We propose a fast semiautomatic video object extraction algorithm which combines a watershed segmentation schemes and chamfer distance transform. Initial object boundaries in the first frame are defined by a human before the tracking, and fast video object tracking can be achieved by tracking only motion-detected regions in a video frame. Experimental results shows that the boundaries of tracking video object arc close to real video object boundaries and the proposed algorithm is promising in terms of speed.

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EFFICIENT MULTIVIEW VIDEO CODING BY OBJECT SEGMENTATION

  • Boonthep, Narasak;Chiracharit, Werapon;Chamnongthai, Kosin;Ho, Yo-Sung
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2009.01a
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    • pp.294-297
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    • 2009
  • Multi-view video consists of a set of multiple video sequences from multiple viewpoints or view directions in the same scene. It contains extremely a large amount of data and some extra information to be stored or transmitted to the user. This paper presents inter-view correlations among video objects and the background to reduce the prediction complexity while achieving a high coding efficiency in multi-view video coding. Our proposed algorism is based on object-based segmentation scheme that utilizes video object information obtained from the coded base view. This set of data help us to predict disparity vectors and motion vectors in enhancement views by employing object registration, which leads to high compression and low-complexity coding scheme for enhancement views. An experimental results show that the superiority can provide an improvement of PSNR gain 2.5.3 dB compared to the simulcast.

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A Real-time SoC Design of Foreground Object Segmentation (Foreground 객체 추출을 위한 실시간 SoC 설계)

  • Kim Ji-Su;Lee Tae-Ho;Lee Hyuk-Jae
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.43 no.9 s.351
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    • pp.44-52
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    • 2006
  • Recently developed MPEG-4 Part 2 compression standard provides a novel capability to handle arbitrary video objects. To support this capability, an efficient object segmentation technique is required. This paper proposes a real-time algorithm for foreground object segmentation in video sequences. The proposed algorithm consists of two steps: the first step that segments a video frame into multiple sub-regions using Spatio-Temporal Watershed Transform and the second step in which a foreground object segment is extracted from the sub-regions generated in the first step. For real-time processing, the algorithm is partitioned into hardware and software parts so that computationally expensive parts are off-loaded from a processor and executed by hardware accelerators. Simulation results show that the proposed implementation can handle QCIF-size video at 15 fps and extracts an accurate foreground object.

Data Augmentation Scheme for Semi-Supervised Video Object Segmentation (준지도 비디오 객체 분할 기술을 위한 데이터 증강 기법)

  • Kim, Hojin;Kim, Dongheyon;Kim, Jeonghoon;Im, Sunghoon
    • Journal of Broadcast Engineering
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    • v.27 no.1
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    • pp.13-19
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    • 2022
  • Video Object Segmentation (VOS) task requires an amount of labeled sequence data, which limits the performance of the current VOS methods trained with public datasets. In this paper, we propose two effective data augmentation schemes for VOS. The first augmentation method is to swap the background segment to the background from another image, and the other method is to play the sequence in reverse. The two augmentation schemes for VOS enable the current VOS methods to robustly predict the segmentation labels and improve the performance of VOS.

Real-Time Moving Object Detection and Shadow Removal in Video Surveillance System (비디오 감시 시스템에서 실시간 움직이는 물체 검출 및 그림자 제거)

  • Lee, Young-Sook;Chung, Wan-Young
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2009.10a
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    • pp.574-578
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    • 2009
  • Real-time object detection for distinguishing a moving object of interests from the background image in still image or video image sequence is an essential step to a correct object tracking and recognition. Moving cast shadow can be misclassified as part of objects or moving objects because the shadow region is included in the moving object region after object segmentation. For this reason, an algorithm for shadow removal plays an important role in the results of accurate moving object detection and tracking systems. To handle with the problems, an accurate algorithm based on the features of moving object and shadow in color space is presented in this paper. Experimental results show that the proposed algorithm is effective to detect a moving object and to remove shadow in test video sequences.

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