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首页> 外文期刊>Journal of electrical and computer engineering >Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling
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Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling

机译：平移和抖动不变的增量主成分追求视频背景建模

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摘要

Video background modeling is an important preprocessing stage for various applications, and principal component pursuit (PCP) is among the state-of-the-art algorithms for this task. One of the main drawbacks of PCP is its sensitivity to jitter and camera movement. This problem has only been partially solved by a few methods devised for jitter or small transformations. However, such methods cannot handle the case of moving or panning cameras in an incremental fashion. In this paper, we greatly expand the results of our earlier work, in which we presented a novel, fully incremental PCP algorithm, named incPCP-PTI, which was able to cope with panning scenarios and jitter by continuously aligning the low-rank component to the current reference frame of the camera. To the best of our knowledge, incPCP-PTI is the first low-rank plus additive incremental matrix method capable of handling these scenarios in an incremental way. The results on synthetic videos and Moseg, DAVIS, and CDnet2014 datasets show that incPCP-PTI is able to maintain a good performance in the detection of moving objects even when panning and jitter are present in a video. Additionally, in most videos, incPCP-PTI obtains competitive or superior results compared to state-of-the-art batch methods.
机译：视频背景建模是各种应用的重要预处理阶段，主要成分追求（PCP）是此任务的最先进的算法之一。 PCP的主要缺点之一是对抖动和相机运动的敏感性。此问题仅通过设计抖动或小型转换的几种方法部分解决。然而，这种方法不能以增量方式处理移动或平移摄像机的情况。在本文中，我们大大扩展了我们之前工作的结果，其中我们提出了一种名为IncPCP-PTI的新颖全增量PCP算法，该算法通过不断对准低秩分量来应对平移方案和抖动相机的当前参考帧。据我们所知，IncPCP-PTI是能够以增量方式处理这些场景的第一个低级加附加增量矩阵方法。结果对合成视频和MOSEG，DAVIS和CDNET2014数据集显示，即使在视频中存在平移和抖动，IncPCP-PTI也能够在检测到移动物体中的良好性能。此外，在大多数视频中，与最先进的批处理方法相比，IncPCP-PTI获得了竞争力或优越的结果。

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