Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Moving object detection is the foundation of research in many computer vision fields. In
recent decades, a number of detection methods have been proposed. Relevant surveys …

Foreground detection (FD) plays an important role in the domain of video surveillance for
highway. The design of advanced FD algorithms requires large-scale and diverse video …

Anomaly detection through machine sounds plays a crucial role in the development of
industrial automation due to its excellent flexibility and real-time response capabilities …

Background subtraction is a prevalent way of dealing with detecting the local changes from
video scenes. Background subtraction divides an image frame into foreground and …

The background subtraction (BGS) technique is popularly used for many surveillance
systems, segmenting the foreground by subtracting the modeled background from the image …

A roadside sensing unit can provide over-the-horizon perception information for
autonomous vehicles due to its high perception perspective. However, numerous …

We propose a universal background subtraction framework based on the Arithmetic
Distribution Neural Network (ADNN) for learning the distributions of temporal pixels. In our …

Images taken under low light or dim backlight conditions usually have insufficient brightness,
low contrast, and poor visual quality of the image, which leads to increased difficulty in …

Moving Object Segmentation (MOS) is a challenging problem in computer vision, particularly
in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and …