结合l_(1/2)范数与显著性约束的背景减除

Background Subtraction Combining l_(1/2) Norm and Saliency Constraint

传统背景减除模型在背景静止和前景对象移动较快时提取到的前景效果较好,但当背景变化或前景对象移动缓慢时容易将动态背景误判为前景或检测出的前景有较多空洞。针对传统背景减除模型在动态背景和前景对象移动缓慢条件下存在前景检测精度下降的问题,提出一种基于l_(1/2)范数与显著性约束的背景减除模型。将观测数据分为低秩背景、运动前景和动态干扰3类,利用l_(1/2)范数约束运动前景加强前景稀疏性,有效抑制动态背景对前景提取造成的干扰,提高运动前景在动态背景中的检测精度。引入视频每一帧的显著性约束,通过对每一帧图像进行低秩稀疏分解来检测移动缓慢的目标。实验结果表明,该模型对于复杂场景具有较强的适应能力,可有效去除动态背景对前景的干扰,快速检测出移动缓慢的前景对象,相比于l_(1/1/2)-RPCA背景减除模型的平均查全率、查准率和调和平均值分别提升了9、14和10个百分点。

The conventional background subtraction model extracts the foreground more effectively when the background is static and the foreground object propagates rapidly.However,when the background is dynamic or the foreground object propagates slowly,the dynamic background can be easily misjudged as the foreground or more foreground voids are detected.To address the decreasing foreground detection accuracy of conventional background subtraction models in cases involving a dynamic background and slowly propagating foreground objects,a background subtraction model based on the l_(1/2) norm and saliency constraints is proposed.The observation data are classified into three types:low-rank background,motion foreground,and dynamic interference.The l_(1/2) norm is used to constrain the motion foreground and hence strengthen the foreground sparsity,which effectively suppresses the interference caused by the dynamic background to the foreground extraction and improves the performance of the motion foreground in the dynamic background.A saliency constraint for each frame of video is introduced,and slowly propagating objects are detected by performing low-rank sparse decomposition on each frame of images.Experimental results show that the model is highly adaptable to complex scenes,effectively removes the interference of dynamic background on the foreground,and detects slowly propagating foreground objects rapidly.Compared with the l_(1/1/2)-RPCA background subtraction model,the proposed model improves the average recall,precision,and F-measure by 9,14,and 10 percentage points,respectively.