复杂动态背景下基于群稀疏的运动目标检测方法

Moving Object Detection Method Based on Group Sparseness Under Complex Dynamic Background

为提高复杂动态背景下运动目标检测精度,基于低秩及稀疏分解理论,本文提出一种基于群稀疏的运动目标检测方法.所提方法将观测视频分解为低秩静态背景,群稀疏前景及动态背景三部分.所提方法首先使用伽马范数近乎无偏近似矩阵秩函数,以解决核范数过度惩罚较大奇异值导致所得最小化问题无法获得最优解进而降低检测性能的问题;其次,为利用前景目标边界先验信息以提升运动目标检测性能,每一帧使用过分割算法生成同性区域以定义群稀疏范数并用于约束前景矩阵;再次,为避免运动目标同时出现在稀疏前景和动态背景中,引入非相干项以提升二者可分性;最后,本文利用交替方向乘子方法(Alternating Direction Method of Multipliers,ADMM)求解所得非凸目标函数.实验结果表明,与现有主流运动目标检测算法相比,复杂动态背景下本文所提方法可较好抑制动态背景从而显著提高复杂运动背景下运动目标检测精度.

To improve the accuracy of moving object detection under complex dynamic background,based on the the⁃ory of low-rank and sparse decomposition,a group sparse based moving object detection method is developed.The pro⁃posed method decomposes the observed video into a low-rank static background,a group sparse foreground and a dynamic background.Regarding the problem that the nuclear norm over-penalizing large singular values leads to the optimal solution of the obtained minimization problem cannot be obtained and then the detection performance is decreased,the gamma norm is introduced to acquire almost unbiased approximation of rank function.In order to utilize the object boundary prior to en⁃hance the moving target detection performance,each frame is over-segmented into homogeneous regions which are taken to define the group sparse norm to constrain the foreground matrix.Moreover,to prevent the moving object from appearing in the sparse foreground and dynamic background simultaneously,the incoherence term is introduced to enhance the separabil⁃ity of them.Finally,the obtained non-convex objective function can be solved using the alternating direction multiplier method(ADMM).The experimental results show that,compared with the state-of-the-art moving target detection algo⁃rithms,the developed method can suppress the dynamic background considerably and then improve the accuracy of moving object detection significantly under complex dynamic background.