张量低秩表示和时空稀疏分解的视频前景检测

针对视频中前景检测的问题,提出了一种基于张量低秩表示(Tensor Low-Rank Representation,TLRR)和时空稀疏分解的检测方法。由于视频序列中的前景除具有稀疏性外,本身还具有空间上的连续性以及时间上的持续性,本文提出采用时空稀疏范数对前景特性进行深入发掘。利用张量低秩表示方法将原始视频用张量形式进行分解,充分利用了原始数据的行信息和列信息,且将原始的背景、前景二分解泛化为背景、前景和噪声的三分解,使用非精确增广拉格朗日乘子(Inexact Augmented Lagrange Multiplier,IALM)方法进行最优化求解,并对算法进行了分析。设计实验对本文新方法的有效性进行了验证和比较,并对影响算法效果的重要参数ρ进行了进一步研究实验。实验结果表明:该方法能够有效检测出视频中的运动前景,其准确性相对已有方法有一定提高。

Video foreground detection of tensor low-rank representation and spatial-temporal sparsity decomposition

A detection method based on Tensor Low-Rank Representation (TLRR) and spatial-temporal sparsity decomposition was proposed to detect foreground targets in video sequences.Since foreground in video sequence has sparsity inherently besides spatially continuous and temporally continuous,this paper put forward spatial-temporal sparsity-inducing norm to perform deep research on property of foreground.Original video was decomposed in tensor representation formed by tensor low-rank representation method,line information and column information of original data were fully used,and two-stage decomposition of original background and foreground was generalized to three-stage decomposition of background,foreground and noises.Optimization solution was performed with Inexact Augmented Lagrange Multiplier (IALM) method.Verification and comparison experiment was established,and further research experiment was performed to research how ρ affecting performance of algorithm.Experimental results show that the method can detect moving foreground in video effectively and improve accuracy when compared with existing methods.