Tensor learningusing N-mode SVD for dynamic background modelling and subtraction

Sheheryar Khan, Guoxia Xu, Hong Yan
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Abstract

Background modelling and subtraction is an essential component in motion analysis with wide range of applications in computer vision, whereas the task becomes more challenging in context of complex scenarios such as dynamic backgrounds. In this paper, we address the problem of modelling dynamic backgrounds in online tensor leaning framework. We use Tucker decomposition to model thespatio-temporal correlation of video background. To facilitate the online execution of foreground detection, we incrementally update the subspace factor matrices and core tensor by using the N-mode SVD. For the upcoming frame, the estimate of new basis matrix is updated, whereas the contents from last observation are removed. Similarity measure based on pixel values is carried out to produce the foreground mask. Visual analysis on video datasets has revealed that the proposed approach is well suited against dynamically varying backgrounds. Our quantitative results show that the proposed strategy is superior to state-of-the-art methods.
使用n模SVD进行动态背景建模和减法的张量学习
背景建模和减法是运动分析的重要组成部分,在计算机视觉中有着广泛的应用,而在动态背景等复杂场景下,这项任务变得更具挑战性。本文研究了在线张量学习框架中动态背景的建模问题。利用Tucker分解对视频背景的时空相关性进行建模。为了方便在线执行前景检测,我们使用n模SVD增量更新子空间因子矩阵和核心张量。对于即将到来的帧,更新新基矩阵的估计,同时删除上次观测的内容。采用基于像素值的相似度度量来生成前景蒙版。对视频数据集的可视化分析表明,该方法适用于动态变化的背景。我们的定量结果表明,所提出的策略优于最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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