An empirical review of deep learning frameworks for change detection: Model design, experimental frameworks, challenges and research needs
M Mandal, SK Vipparthi - IEEE Transactions on Intelligent …, 2021 - ieeexplore.ieee.org
Visual change detection, aiming at segmentation of video frames into foreground and
background regions, is one of the elementary tasks in computer vision and video analytics …
background regions, is one of the elementary tasks in computer vision and video analytics …
A review on visual privacy preservation techniques for active and assisted living
This paper reviews the state of the art in visual privacy protection techniques, with particular
attention paid to techniques applicable to the field of Active and Assisted Living (AAL). A …
attention paid to techniques applicable to the field of Active and Assisted Living (AAL). A …
Histogram of oriented gradient-based fusion of features for human action recognition in action video sequences
Human Action Recognition (HAR) is the classification of an action performed by a human.
The goal of this study was to recognize human actions in action video sequences. We …
The goal of this study was to recognize human actions in action video sequences. We …
3DCD: Scene independent end-to-end spatiotemporal feature learning framework for change detection in unseen videos
Change detection is an elementary task in computer vision and video processing
applications. Recently, a number of supervised methods based on convolutional neural …
applications. Recently, a number of supervised methods based on convolutional neural …
Scene independency matters: An empirical study of scene dependent and scene independent evaluation for CNN-based change detection
M Mandal, SK Vipparthi - IEEE Transactions on Intelligent …, 2020 - ieeexplore.ieee.org
Visual change detection in video is one of the essential tasks in computer vision
applications. Recently, a number of supervised deep learning methods have achieved top …
applications. Recently, a number of supervised deep learning methods have achieved top …
Deep learning-based moving object segmentation: Recent progress and research prospects
R Jiang, R Zhu, H Su, Y Li, Y Xie, W Zou - Machine Intelligence Research, 2023 - Springer
Moving object segmentation (MOS), aiming at segmenting moving objects from video
frames, is an important and challenging task in computer vision and with various …
frames, is an important and challenging task in computer vision and with various …
An Improved VGG-19 Network Induced Enhanced Feature Pooling For Precise Moving Object Detection In Complex Video Scenes
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 …
objective of any such system is to detect local changes, and the system could be utilized to …
Kernel-induced possibilistic fuzzy associate background subtraction for video scene
The background subtraction (BGS) technique is popularly used for many surveillance
systems, segmenting the foreground by subtracting the modeled background from the image …
systems, segmenting the foreground by subtracting the modeled background from the image …
[PDF][PDF] A survey of efficient deep learning models for moving object segmentation
Moving object segmentation (MOS) is the process of identifying dynamic objects from video
frames, such as moving vehicles or pedestrians, while discarding the background. It plays …
frames, such as moving vehicles or pedestrians, while discarding the background. It plays …
Universal background subtraction based on arithmetic distribution neural network
We propose a universal background subtraction framework based on the Arithmetic
Distribution Neural Network (ADNN) for learning the distributions of temporal pixels. In our …
Distribution Neural Network (ADNN) for learning the distributions of temporal pixels. In our …