Kwak Sooyeong, Bae Guntae, Byun Hyeran
Department of Computer Science, Yonsei University, 134, Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, South Korea.
J Opt Soc Am A Opt Image Sci Vis. 2010 Feb 1;27(2):180-7. doi: 10.1364/JOSAA.27.000180.
This paper describes a real-time foreground segmentation method in monocular video sequences for video teleconferencing. Background subtraction is widely used in foreground segmentation for static cameras. However, the results are usually not accurate enough for background substitution tasks. In this paper, we propose a novel strategy for fast and accurate foreground segmentation. The strategy consists of two steps: initial foreground segmentation and fine foreground segmentation. The key to our algorithm consists of two steps. In the first step, a moving object is roughly segmented using the background subtraction method. In order to update the initial foreground segmentation results in the second step, a region-based segmentation method and a foreground history map (FHM)-based segmentation representing the combination of temporal and spatial information were developed. The segmentation accuracy of the proposed algorithm was evaluated with respect to the ground truth, which was the manually cropped foreground. The experimental results showed that the proposed algorithm improved the accuracy of segmentation with respect to Horprasert's well-known algorithm.
本文描述了一种用于视频电话会议的单目视频序列实时前景分割方法。背景减法在静态相机的前景分割中被广泛使用。然而，其结果对于背景替换任务来说通常不够准确。在本文中，我们提出了一种用于快速准确前景分割的新策略。该策略包括两个步骤：初始前景分割和精细前景分割。我们算法的关键由两个步骤组成。第一步，使用背景减法方法对运动物体进行粗略分割。为了在第二步中更新初始前景分割结果，开发了一种基于区域的分割方法和一种基于前景历史图（FHM）的分割方法，该方法表示时间和空间信息的组合。所提算法的分割精度相对于作为手动裁剪前景的真实情况进行了评估。实验结果表明，相对于Horprasert的著名算法，所提算法提高了分割精度。
