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对于固定摄像机的视频序列,假设背景具有低秩特征,动态前景具有稀疏特性,提出了一种基于低秩稀疏联合表示的运动检测方法.思路如下:通过图像预处理降低视频序列的噪声;估计连续帧之间的光流,生成二进制运动掩模作为运动权重矩阵;基于子空间学习理论,建立了低秩背景与稀疏前景的优化模型;利用ADMM-BCD迭代算法得到视频背景和前景.实验结果表明,该方法优于其他同类运动检测方法,对慢速运动目标检测效果良好.“,”For the video sequences with fixed cameras,it is a reasonable assumption that the fixed background has low-rank characteristic,and the dynamic foreground has sparse characteristic.A new motion detection method based on low-rank and sparse joint representation is proposed in this paper.The ideas of the proposed method are described as follows:The noise of video sequence is removed by image preprocessing.The optical flow between continuous video sequences is estimated,which is used to generate a binary motion mask as a movement weight matrix.An optimization model with low-rank background and sparse foreground is established based on the idea of subspace learning theory.The background and foreground of each frame are obtained by using the ADMM-BCD iterative algorithm.Experimental results show that the proposed method is super to the other same sort of moving detection methods.The proposed method has perfect effect on slow moving target detection.