Accurate extraction of foregrounds in videos is one of the challenging problems in computer vision. In this study, we propose dynamic mode decomposition via dictionary learning (dl-DMD), which is applied to extract moving objects by separating the sequence of video frames into foreground and background information with a dictionary learned using block patches on the video frames. Dynamic mode decomposition (DMD) decomposes spatiotemporal data into spatial modes, each of whose temporal behavior is characterized by a single frequency and growth/decay rate and is applicable to split a video into foregrounds and the background when applying it to a video. And, in dl-DMD, DMD is applied on coefficient matrices estimated over a learned dictionary, which enables accurate estimation of dynamical information in videos. Due to this scheme, dl-DMD can analyze the dynamics of respective regions in a video based on estimated amplitudes and temporal evolution over patches. The results on synthetic data exhibit that dl-DMD outperforms the standard DMD and compressed DMD (cDMD) based methods. Also, the results of an empirical performance evaluation in the case of foreground extraction from videos using publicly available dataset demonstrates the effectiveness of the proposed dl-DMD algorithm and achieves a performance that is comparable to that of the state-of-the-art techniques in foreground extraction tasks.

正确提取视频中的前景是计算机视觉中的难题之一。在这项研究中，我们提出了通过字典学习（dl-DMD）进行动态模式分解的方法，该方法用于通过使用视频块上的块补丁学习的字典将视频帧的序列分为前景和背景信息来提取运动对象。动态模式分解（DMD）将时空数据分解为空间模式，每个时态数据的时间行为均以单个频率和增长率/衰减率为特征，适用于将视频应用于视频时将其分为前景和背景。并且，在dl-DMD中，将DMD应用于通过学习词典估计的系数矩阵，从而可以准确估计视频中的动态信息。由于这个计划，dl-DMD可以基于估计的幅度和面片上的时间演变来分析视频中各个区域的动态。综合数据的结果表明，dl-DMD优于基于标准DMD和基于压缩DMD（cDMD）的方法。此外，在使用公开数据集从视频中进行前景提取的情况下，经验性能评估的结果证明了所提出的dl-DMD算法的有效性，并获得了与最新技术相当的性能在前台提取任务中。