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Deep Neural Network Concepts for Background Subtraction: A Systematic
  Review and Comparative Evaluation

Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation

13 November 2018
T. Bouwmans
S. Javed
M. Sultana
Soon Ki Jung
ArXivPDFHTML

Papers citing "Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation"

22 / 22 papers shown
Title
Learning Temporal Distribution and Spatial Correlation Towards Universal
  Moving Object Segmentation
Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation
Guanfang Dong
Chenqiu Zhao
Xichen Pan
Anup Basu
VOS
29
3
0
19 Apr 2023
ZBS: Zero-shot Background Subtraction via Instance-level Background
  Modeling and Foreground Selection
ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection
Yongqi An
Xu Zhao
Tao Yu
Haiyun Guo
Chaoyang Zhao
Ming Tang
Jinqiao Wang
40
20
0
26 Mar 2023
Graph CNN for Moving Object Detection in Complex Environments from
  Unseen Videos
Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos
Jhony H. Giraldo
S. Javed
Naoufel Werghi
T. Bouwmans
27
26
0
13 Jul 2022
Optical flow-based branch segmentation for complex orchard environments
Optical flow-based branch segmentation for complex orchard environments
A. You
C. Grimm
J. Davidson
23
9
0
26 Feb 2022
An exploration of the performances achievable by combining unsupervised
  background subtraction algorithms
An exploration of the performances achievable by combining unsupervised background subtraction algorithms
Sébastien Piérard
Marc Braham
Marc Van Droogenbroeck
21
0
0
25 Feb 2022
A Study of the Generalizability of Self-Supervised Representations
A Study of the Generalizability of Self-Supervised Representations
Atharva Tendle
Mohammad Rashedul Hasan
76
27
0
19 Sep 2021
An Empirical Review of Deep Learning Frameworks for Change Detection:
  Model Design, Experimental Frameworks, Challenges and Research Needs
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs
Murari Mandal
Santosh Kumar Vipparthi
27
83
0
04 May 2021
Universal Background Subtraction based on Arithmetic Distribution Neural
  Network
Universal Background Subtraction based on Arithmetic Distribution Neural Network
Chenqiu Zhao
Kang-Ting Hu
Anup Basu
32
21
0
16 Apr 2021
BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic
  Supervised Background Subtraction
BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction
M. Tezcan
Prakash Ishwar
Janusz Konrad
21
77
0
23 Jan 2021
Video Summarization Using Deep Neural Networks: A Survey
Video Summarization Using Deep Neural Networks: A Survey
Evlampios Apostolidis
E. Adamantidou
Alexandros I. Metsai
Vasileios Mezaris
Ioannis Patras
AI4TS
69
202
0
15 Jan 2021
Advances in Electron Microscopy with Deep Learning
Advances in Electron Microscopy with Deep Learning
Jeffrey M. Ede
35
2
0
04 Jan 2021
"What's This?" -- Learning to Segment Unknown Objects from Manipulation
  Sequences
"What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences
W. Boerdijk
M. Sundermeyer
M. Durner
Rudolph Triebel
19
7
0
06 Nov 2020
Review: Deep Learning in Electron Microscopy
Review: Deep Learning in Electron Microscopy
Jeffrey M. Ede
34
79
0
17 Sep 2020
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions
W. Boerdijk
M. Sundermeyer
M. Durner
Rudolph Triebel
30
8
0
11 Feb 2020
Moving Objects Detection with a Moving Camera: A Comprehensive Review
Moving Objects Detection with a Moving Camera: A Comprehensive Review
Marie-Neige Chapel
T. Bouwmans
29
93
0
15 Jan 2020
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised
  Anomaly Detection
Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection
A. Akhriev
Jakub Mareˇcek
UQCV
32
4
0
09 Dec 2019
DeepPBM: Deep Probabilistic Background Model Estimation from Video
  Sequences
DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences
Amirreza Farnoosh
B. Rezaei
Sarah Ostadabbas
BDL
31
15
0
03 Feb 2019
Background Subtraction in Real Applications: Challenges, Current Models
  and Future Directions
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
T. Bouwmans
B. G. García
26
269
0
11 Jan 2019
Background Subtraction with Real-time Semantic Segmentation
Background Subtraction with Real-time Semantic Segmentation
Dongdong Zeng
Xiang Chen
Ming Zhu
Michael Goesele
Arjan Kuijper
VOS
44
44
0
25 Nov 2018
Tensor Robust Principal Component Analysis with A New Tensor Nuclear
  Norm
Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
Canyi Lu
Jiashi Feng
Yudong Chen
Wei Liu
Zhouchen Lin
Shuicheng Yan
56
737
0
10 Apr 2018
DehazeNet: An End-to-End System for Single Image Haze Removal
DehazeNet: An End-to-End System for Single Image Haze Removal
Bolun Cai
Xiangmin Xu
Kui Jia
Chunmei Qing
Dacheng Tao
119
2,402
0
28 Jan 2016
MatConvNet - Convolutional Neural Networks for MATLAB
MatConvNet - Convolutional Neural Networks for MATLAB
Andrea Vedaldi
Karel Lenc
183
2,946
0
15 Dec 2014
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