Moving object segmentation (MOS), aiming at segmenting moving objects from video
frames, is an important and challenging task in computer vision and with various …

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 …

Moving object segmentation (MOS) in videos received considerable attention because of its
broad security-based applications like robotics, outdoor video surveillance, self-driving cars …

Background subtraction is an essential task in computer vision, and is often used as a pre-
processing step for many advanced tasks. In this work, we propose a novel multi-scale …

Visual surveillance requires robust detection of foreground objects under challenging
environments of abrupt lighting variation, stationary foreground objects, dynamic …

Local change detection for moving object detection is an essential step in any computer
vision task. The most well-known technique is background subtraction BGS. However, the …

Recently, the fractional Fourier transform (FrFT) has been integrated into distinct deep
neural network (DNN) models such as transformers, sequence models, and convolutional …

Federated Averaging (FedAvg) is the baseline Federated Learning (FL) algorithm that
applies the stochastic gradient descent for local model training and the arithmetic averaging …

Object change detection (OCD), which aims to segment moving objects from an input frame,
has attracted growing attention. Most OCD algorithms rely on scene diversity or ignore the …

Automated video-based applications are a highly demanding technique from a security
perspective, where detection of moving objects ie, moving object segmentation (MOS) is …