Background subtraction is an effective method of choice when it comes to detection of
moving objects in videos and has been recognized as a breakthrough for the wide range of …

Low-rank tensor completion aims to recover the missing entries of multi-way data, which has
become popular and vital in many fields such as signal processing and computer vision. It …

The paper provides a specific perspective view on background subtraction for moving object
detection, as a building block for many computer vision applications, being the first relevant …

The recent popular tensor tubal rank, defined based on tensor singular value decomposition
(t-SVD), yields promising results. However, its framework is applicable only to three-way …

The tensor nuclear norm (TNN), defined as the sum of nuclear norms of frontal slices of the
tensor in a frequency domain, has been found useful in solving low-rank tensor recovery …

Eye blink detection is a challenging problem that many researchers are working on because
it has the potential to solve many facial analysis tasks, such as face anti-spoofing, driver …

Robust PCA (RPCA) and its tensor extension, namely, Robust Tensor PCA (RTPCA),
provide an effective framework for background/foreground separation by decomposing the …

Recently, the transform-based tensor nuclear norm (TNN) methods have shown promising
performance and drawn increasing attention in tensor completion (TC) problems. The main …

Intelligent video surveillance is a vital technique in smart city construction, where detection
of surveillance objects is generally achieved by subtracting estimated background from the …

By minimizing the tensor ranks, recent methods exploit the tensor spatial correlation
between the tensor entries for the low-rank tensor completion (TC) problem. However, these …