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首页> 外文期刊>International Journal of Innovative Computing Information and Control >REAL-TIME ON-LINE VIDEO OBJECT SEGMENTATION BASED ON MOTION DETECTION WITHOUT BACKGROUND CONSTRUCTION
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REAL-TIME ON-LINE VIDEO OBJECT SEGMENTATION BASED ON MOTION DETECTION WITHOUT BACKGROUND CONSTRUCTION

机译：基于运动检测的实时在线视频对象分割，无需背景构造

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摘要

A novel scheme for real-time on-line video object segmentation without back ground construction is presented. The proposed method uses foreground extraction-based video object segmentation. Motion and gradient-variant information is used to quickly acquire a coarse moving object mask. Compensation for still regions in a moving object is also proposed. Noise elimination, morphological processing and connected component labeling are used to obtain the fine moving object mask. Finally, moving object refinement (object boundary refinement, region growth/compensation and object region refinement) is used to overcome the residual background problem in order to obtain more accurate video object segmentation. Experimental results show that the proposed method has good spatial accuracy, sensitivity, specificity and execution time. Objective evaluation results of the proposed method indicate that the average sensitivity, specificity and spatial accu racy can be maintained at 98.49%, 99.31% and 97.77%, respectively, for the tested video sequences.
机译：提出了一种无需背景构造的实时在线视频目标分割的新方案。所提出的方法使用基于前景提取的视频对象分割。运动和梯度变化信息用于快速获取粗糙的运动对象蒙版。还提出了对运动物体中静止区域的补偿。使用噪声消除，形态学处理和连接的组件标记来获得精细的移动物体蒙版。最后，运动对象细化（对象边界细化，区域增长/补偿和对象区域细化）用于克服残留背景问题，以获得更准确的视频对象分割。实验结果表明，该方法具有良好的空间准确性，敏感性，特异性和执行时间。所提方法的客观评估结果表明，对于所测试的视频序列，平均灵敏度，特异性和空间精度可以分别保持在98.49％，99.31％和97.77％。

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