School of Electronic Engineering, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2020 Aug 14;20(16):4558. doi: 10.3390/s20164558.
Detecting and removing ghosts is an important challenge for moving object detection because ghosts will remain forever once formed, leading to the overall detection performance degradation. To deal with this issue, we first classified the ghosts into two categories according to the way they were formed. Then, the sample-based two-layer background model and histogram similarity of ghost areas were proposed to detect and remove the two types of ghosts, respectively. Furthermore, three important parameters in the two-layer model, i.e., the distance threshold, similarity threshold of local binary similarity pattern (LBSP), and time sub-sampling factor, were automatically determined by the spatial-temporal information of each pixel for adapting to the scene change rapidly. The experimental results on the CDnet 2014 dataset demonstrated that our proposed algorithm not only effectively eliminated ghost areas, but was also superior to the state-of-the-art approaches in terms of the overall performance.
检测和去除鬼影是运动目标检测的一个重要挑战，因为鬼影一旦形成就会永远存在，导致整体检测性能下降。针对这个问题，我们首先根据鬼影的形成方式将其分为两类。然后，提出了基于样本的双层背景模型和鬼影区域的直方图相似度，分别用于检测和去除这两种类型的鬼影。此外，两层模型中的三个重要参数，即距离阈值、局部二值模式（LBSP）相似性的相似性阈值和时间子采样因子，通过每个像素的时空信息自动确定，以适应快速变化的场景。在 CDnet 2014 数据集上的实验结果表明，我们提出的算法不仅有效地消除了鬼影区域，而且在整体性能方面也优于最先进的方法。
