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Predefined object tracking method for video segmentation

机译:用于视频分割的预定义对象跟踪方法

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Abstract: In order to support the philosophy of the MPEG-4 video coding standard, we have to represent each frame of video sequences in terms of video object planes (VOPs). Several automatic methods for segmenting of moving objects have been developed. Such algorithms separate the foreground from the background with a change detection mask, which is obtained by the difference image between two successive frames. Thus, these techniques cannot represent each individual video object in a single frame separately, i.e., object correspondence problem. In addition, those algorithms are somewhat premature to obtain desirable segmentation results from all kinds of image sequences because the mathematical model or the similarity measure for the extraction of the video object has not been defined adequately. However, if the user can define video objects in the first frame or newly appeared video objects by a partially or completely user-assisted method like the snake's algorithm, we may obtain good segmentation results over the following successive frames. This semi-automatic segmentation may be more practical in generating VOPs of moving objects. In this paper, we propose a new user-assisted video segmentation algorithm. This algorithm consists of two steps: intra-frame segmentation and inter-frame segmentation. The intra-frame segmentation is applied to the first frame of the image sequence or the frames that have newly appeared video objects. The user can manually define the newly appeared video objects in the image sequence. The inter-frame segmentation is applied to the following consecutive frames. In the inter-frame segmentation, user-defined video objects are segmented automatically by object tracking.!11
机译:摘要:为了支持MPEG-4视频编码标准的理念,我们必须用视频对象平面(VOP)表示视频序列的每一帧。已经开发了用于分割运动对象的几种自动方法。这种算法使用变化检测掩码将前景与背景分开,该变化检测掩码是通过两个连续帧之间的差异图像获得的。因此,这些技术不能在单个帧中分别表示每个单独的视频对象,即对象对应问题。另外,由于尚未充分定义用于提取视频对象的数学模型或相似性度量,因此这些算法在某种程度上不能从各种图像序列中获得理想的分割结果,还为时过早。但是,如果用户可以通过部分或完全由用户辅助的方法(例如,snake算法)在第一帧中定义视频对象或在新出现的视频对象中进行定义,则我们可以在随后的连续帧中获得良好的分割结果。在生成运动对象的VOP时,这种半自动分段可能更实用。在本文中,我们提出了一种新的用户辅助视频分割算法。该算法包括两个步骤:帧内分割和帧间分割。帧内分割应用于图像序列的第一帧或具有新出现的视频对象的帧。用户可以手动定义图像序列中新出现的视频对象。帧间分割被应用于随后的连续帧。在帧间分割中,通过对象跟踪自动分割用户定义的视频对象。!11

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