Li Wenhui, Zhang Jianqi, Wang Ying
College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Sensors (Basel). 2019 Jun 13;19(12):2672. doi: 10.3390/s19122672.
The pixel-based adaptive segmenter (PBAS) is a classic background modeling algorithm for change detection. However, it is difficult for the PBAS method to detect foreground targets in dynamic background regions. To solve this problem, based on PBAS, a weighted pixel-based adaptive segmenter named WePBAS for change detection is proposed in this paper. WePBAS uses weighted background samples as a background model. In the PBAS method, the samples in the background model are not weighted. In the weighted background sample set, the low-weight background samples typically represent the wrong background pixels and need to be replaced. Conversely, high-weight background samples need to be preserved. According to this principle, a directional background model update mechanism is proposed to improve the segmentation performance of the foreground targets in the dynamic background regions. In addition, due to the "background diffusion" mechanism, the PBAS method often identifies small intermittent motion foreground targets as background. To solve this problem, an adaptive foreground counter was added to the WePBAS to limit the "background diffusion" mechanism. The adaptive foreground counter can automatically adjust its own parameters based on videos' characteristics. The experiments showed that the proposed method is competitive with the state-of-the-art background modeling method for change detection.
基于像素的自适应分割器（PBAS）是一种用于变化检测的经典背景建模算法。然而，PBAS方法在动态背景区域中检测前景目标存在困难。为了解决这个问题，本文基于PBAS提出了一种用于变化检测的加权像素自适应分割器WePBAS。WePBAS使用加权背景样本作为背景模型。在PBAS方法中，背景模型中的样本未加权。在加权背景样本集中，低权重背景样本通常表示错误的背景像素，需要被替换。相反，高权重背景样本需要被保留。根据这一原则，提出了一种定向背景模型更新机制，以提高动态背景区域中前景目标的分割性能。此外，由于“背景扩散”机制，PBAS方法经常将小的间歇性运动前景目标识别为背景。为了解决这个问题，在WePBAS中添加了一个自适应前景计数器来限制“背景扩散”机制。自适应前景计数器可以根据视频的特征自动调整自身参数。实验表明，该方法在变化检测方面与当前最先进的背景建模方法具有竞争力。
