A change detection approach to moving object detection in low fame-rate video
A change detection approach to moving object detection in low fame-rate video
复制标题
低帧率视频中运动物体检测的变化检测方法
DOI：
10.1117/12.818622
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
J. Theiler
中科院分区：
文献类型：
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作者：
R. Porter;N. Harvey;J. Theiler
运动目标检测是许多目标识别和跟踪应用中的第一步，因此在时间图像分析中具有重要意义。几乎所有运动目标检测算法中的一个关键组件是像素级分类器，其中每个像素被预测为运动目标的一部分或背景的一部分。在本文中，我们调查的像素级分类问题的变化检测方法，并评估其对运动目标检测的影响。我们研究的变化检测方法以前应用于多光谱和高光谱数据集，其中图像通常间隔几天或几个月拍摄。在本文中，我们将该方法应用于低帧速率（每秒1-2帧）的视频数据集。
Moving object detection is of significant interest in temporal image analysis since it is a first step in many object identification and tracking applications. A key component in almost all moving object detection algorithms is a pixellevel classifier, where each pixel is predicted to be either part of a moving object or part of the background. In this paper we investigate a change detection approach to the pixel-level classification problem and evaluate its impact on moving object detection. The change detection approach that we investigate was previously applied to multi- and hyper-spectral datasets, where images were typically taken several days, or months apart. In this paper, we apply the approach to lowframe rate (1-2 frames per second) video datasets.