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 Segmentation (MOS) is a fundamental task in computer vision. Due to
undesirable variations in the background scene, MOS becomes very challenging for static …

Background subtraction (BGS) is a fundamental video processing task which is a key
component of many applications. Deep learning-based supervised algorithms achieve very …

Background subtraction is a basic task in computer vision and video processing often
applied as a pre-processing step for object tracking, people recognition, etc. Recently, a …

To address the challenges of change detection in the wild, we present a novel background
subtraction algorithm based on parallel vision and Bayesian generative adversarial …

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 …

Background subtraction (BGS) aims to extract all moving objects in the video frames to
obtain binary foreground segmentation masks. Deep learning has been widely used in this …

In this paper, we propose a method for calculating the dynamic background region in a
video and removing false positives in order to overcome the problems of false positives that …

Background subtraction is a crucial stage in many visual surveillance systems. The prime
objective of any such system is to detect local changes, and the system could be utilized to …

The moving object segmentation (MOS) in videos with bad weather, irregular motion of
objects, camera jitter, shadow and dynamic background scenarios is still an open problem …