【摘要】 运动目标检测是机器视觉领域中的关键技术之一,其在视频运动目标检测、遥感信息处理和军事侦察等领域有广泛的应用。考虑到视频中相邻视频帧背景相似性高且时间连续性长,而阴影和噪声具有非连续性的特征,文中提出一种时间连续性约束的低秩分解背景更新模型,并将其应用于背景模型减除的视频运动目标检测。该方法首先对视频进行低秩分解,获得低秩分量和稀疏分量;然后基于连续性约束背景更新模型更新低秩分量,构建背景;最后通过背景减除及自适应阈值分割获得运动目标。实验结果表明,无论是FM指标还是ROC曲线都反映出所提方法相比目前较好的背景减除方法能够有效克服阴影和噪声的影响,避免"空洞",更准确地提取运动目标,且鲁棒性好。更多还原

【Abstract】 Moving target detection is one of the key technologies in the field of machine vision.Moving object detection is widely used in video moving object detection,remote sensing information processing and military reconnaissance,etc.Considering that the background similarity of adjacent video frames is high,and the shadow and noise are disconti-nuous,this paper proposed a low-rank decomposition background updating model with time continuity constraint,and applied it to the moving object detection of background subtraction.Firstly,low-rank components and sparse components are obtained by using low-rank decomposition.Then the background is constructed by updating the low-rank components based on time continuity constrained.Finally,moving object is obtained by background subtraction and adaptive threshold segmentation.Experimental results show that both the FM index and the ROC curve reflect that compared with the state-of-the-art background subtraction methods,this method can effectively overcome the influence of shadow and noise,reduce holes,extract moving objects more accurately,and has good robustness.更多还原