Conventional neural networks have been demonstrated to be a powerful framework for
background subtraction in video acquired by static cameras. Indeed, the well-known Self …

Several methods have been proposed to solve moving objects segmentation problem
accurately in different scenes. However, many of them lack the ability of handling various …

Background subtraction is usually based on low-level or hand-crafted features such as raw
color components, gradients, or local binary patterns. As an improvement, we present a …

Foreground segmentation algorithms aim at segmenting moving objects from the
background in a robust way under various challenging scenarios. Encoder–decoder-type …

The increasing urban population in cities necessitates the need for the development of smart
cities that can offer better services to its citizens. Drone technology plays a crucial role in the …

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 …

Describing what has changed in a scene can be useful to a user, but only if generated text
focuses on what is semantically relevant. It is thus important to distinguish distractors (eg a …

Background subtraction techniques are often treated as fundamental and significant ways to
analyze and understand video content. In this paper, we propose a weight-sample-based …

We introduce the notion of semantic background subtraction, a novel framework for motion
detection in video sequences. The key innovation consists to leverage object-level …