运动目标检测的l0群稀疏RPCA模型及其算法

经典的鲁棒主成分分析(Robust Principal Component Analysis,RPCA)目标检测算法使用l₁范数逐一判别每一像素点是否属于运动目标,未能考虑到运动目标在空间分布的连续性,不利于提升运动目标检测的鲁棒性.本文提出了一种基于l₀群稀疏RPCA模型的运动目标检测方法.首先运用Ncuts算法进行区域过分割,生成多个同性区域,将其作为群稀疏约束的分组信息;第二步构造基于l₀群稀疏RPCA模型,运用群稀疏准则判别过分割后的各同性区域是否为运动目标,采用交替方向乘子算法对模型进行快速求解,约束过分割形成的同性区域具有相同检测结果,进而将背景环境和运动前景分离,能够更加准确地度量运动目标的区域边界,且对复杂的背景扰动更加鲁棒,达到了运动目标鲁棒检测的目的.

l0 Group Sparse RPCA Model and Algorithm for Moving Object Detection

Classical robust principal component analysis ( RPCA) algorithm uses l1-norm to discriminate whether each pixel belongs to motion object,without taking advantage of the spatial continuity distribution of the movement object. Thus,the accuracy and robustness of detection algorithm is negatively influenced.In order to use the spatial continuity prior, this paper proposes an object detection method based on l0 group sparse regularized RPCA model.First,our algorithm over-segments the video sequence into multiple homogeneous groups using normalized cuts over-segmentation algorithm, and these groups are treated as grouping information for group sparse constraint.The next step is to construct l0 group sparse reg-ularized RPCA model,which uses l0 group sparse criterion to discriminate whether each homogeneous group belongs to mo-tion object.Alternative direction of multiplier method ( ADMM) is adopted to solve our l0 group sparse RPCA model quick-ly.Homogeneous groups are restricted to have the same detection results.Thus,our model can be more robust to background movement and increase the detection accuracy of object boundary.