Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by link...Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by linking Gaussian mixture model with the method of principal component analysis PCA. This approach utilizes the advantage of the PCA method in providing the projections that capture the most relevant pixels for segmentation within the background models. We report the update on both the parameters of the modified method and that of the Gaussian mixture model. The obtained results show the relatively outperform of the integrated method.展开更多
若在建模时存在目标,部分目标像素会进入背景模型,会在检测时产生“鬼影”。为了有效抑制“鬼影”,提出一种利用混合高斯和拓扑结构(Gaussian mixture model and topological structure,GMMT)的人体“鬼影”抑制算法。算法分为两个阶段...若在建模时存在目标,部分目标像素会进入背景模型,会在检测时产生“鬼影”。为了有效抑制“鬼影”,提出一种利用混合高斯和拓扑结构(Gaussian mixture model and topological structure,GMMT)的人体“鬼影”抑制算法。算法分为两个阶段,背景建模阶段采用双通道建模,通道一利用混合高斯模型进行预检测,接着利用拓扑结构将分散的人体目标连接获得完整的目标并取其外接矩形,然后将矩形外的像素加入背景模型,经过多帧的建模得到空背景;通道二使用多帧平均法计算背景模型。通过设置建模帧数的阈值T选择建模方式,若建模帧数小于T则使用通道一建模,否则使用双通道联合建模。目标检测阶段利用改进的背景差分法实现人体分割并进一步消除“鬼影”。经过测试,GMMT在建模阶段存在目标的情况下可有效地抑制“鬼影”。展开更多
文摘Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by linking Gaussian mixture model with the method of principal component analysis PCA. This approach utilizes the advantage of the PCA method in providing the projections that capture the most relevant pixels for segmentation within the background models. We report the update on both the parameters of the modified method and that of the Gaussian mixture model. The obtained results show the relatively outperform of the integrated method.
文摘若在建模时存在目标,部分目标像素会进入背景模型,会在检测时产生“鬼影”。为了有效抑制“鬼影”,提出一种利用混合高斯和拓扑结构(Gaussian mixture model and topological structure,GMMT)的人体“鬼影”抑制算法。算法分为两个阶段,背景建模阶段采用双通道建模,通道一利用混合高斯模型进行预检测,接着利用拓扑结构将分散的人体目标连接获得完整的目标并取其外接矩形,然后将矩形外的像素加入背景模型,经过多帧的建模得到空背景;通道二使用多帧平均法计算背景模型。通过设置建模帧数的阈值T选择建模方式,若建模帧数小于T则使用通道一建模,否则使用双通道联合建模。目标检测阶段利用改进的背景差分法实现人体分割并进一步消除“鬼影”。经过测试,GMMT在建模阶段存在目标的情况下可有效地抑制“鬼影”。
