The aim of the work is to develop algorithms for analyzing video data in real time based on
computer vision methods, as well as deep learning technologies for artificial neural networks …

Visual surveillance has been a very active research topic in the last few years due to its
growing importance in security, law enforcement, and military applications. More and more …

The paper deals with the development of mathematical models and algorithms for video
processing in digital video surveillance systems to detect moving objects. The model and …

Nowadays indoor and outdoor video surveillance systems are very widespread. Earlier, in
the days of the first surveillance systems, the processing power allowed only monitoring and …

In surveillance area the detecting human beings accurately in a visual surveillance system is
crucial for diverse application areas including abnormal event detection. Detect an object …

In the paper research on foreground object segmentation in dynamic background scenarios
(ie flowing water, moving leaves or shrubs) is described. The effectiveness of different …

Visual surveillance systems have long been in use to gather information, to monitor people,
events and activities. The main objective is to compensate the conventional passive video …

The paper considers aspects of Unmanned Aerial Vehicle video stream foreground
detection based on computer vision and artificial intelligence. Nonparametric approach for …

Identifying stationary background from moving objects in a video is a critical task in many
video processing and computer-vision applications. A basic approach is to perform …

Background Modeling forms a central module in systems using Computer Vision to detect
events of interest in a video stream. Most current methods use only the intensity observed at …