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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

End-to-end video background subtraction with 3d convolutional neural networks
Dimitrios Sakkos, Heng Liu, Jungong Han, et al.
Multimedia Tools and Applications (2017) Vol. 77, Iss. 17, pp. 23023-23041
Closed Access | Times Cited: 119

Showing 1-25 of 119 citing articles:

Sensor-based and vision-based human activity recognition: A comprehensive survey
L. Minh Dang, Kyungbok Min, Hanxiang Wang, et al.
Pattern Recognition (2020) Vol. 108, pp. 107561-107561
Closed Access | Times Cited: 530
Deep neural network concepts for background subtraction:A systematic review and comparative evaluation
Thierry Bouwmans, Sajid Javed, M. Sultana, et al.
Neural Networks (2019) Vol. 117, pp. 8-66
Open Access | Times Cited: 356
Background subtraction in real applications: Challenges, current models and future directions
Belmar García-García, Thierry Bouwmans, Alberto Rosales
Computer Science Review (2019) Vol. 35, pp. 100204-100204
Open Access | Times Cited: 292
Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors
Junxuan Zhao, Hao Xu, Hongchao Liu, et al.
Transportation Research Part C Emerging Technologies (2019) Vol. 100, pp. 68-87
Closed Access | Times Cited: 279
Foreground segmentation using convolutional neural networks for multiscale feature encoding
Long Ang Lim, Hacer Yalım Keleş
Pattern Recognition Letters (2018) Vol. 112, pp. 256-262
Open Access | Times Cited: 278
Learning multi-scale features for foreground segmentation
Long Ang Lim, Hacer Yalım Keleş
Pattern Analysis and Applications (2019) Vol. 23, Iss. 3, pp. 1369-1380
Open Access | Times Cited: 193
Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey
H. T. Duong, Viet-Tuan Le, Vinh Truong Hoang
Sensors (2023) Vol. 23, Iss. 11, pp. 5024-5024
Open Access | Times Cited: 46
A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation
Thangarajah Akilan, Q. M. Jonathan Wu, Amin Safaei, et al.
IEEE Transactions on Intelligent Transportation Systems (2019) Vol. 21, Iss. 3, pp. 959-971
Closed Access | Times Cited: 140
BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos
M. Ozan Tezcan, Prakash Ishwar, Janusz Konrad
(2020)
Open Access | Times Cited: 116
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs
Murari Mandal, Santosh Kumar Vipparthi
IEEE Transactions on Intelligent Transportation Systems (2021) Vol. 23, Iss. 7, pp. 6101-6122
Open Access | Times Cited: 88
Perception consistency ultrasound image super-resolution via self-supervised CycleGAN
Heng Liu, Jianyong Liu, Shudong Hou, et al.
Neural Computing and Applications (2021) Vol. 35, Iss. 17, pp. 12331-12341
Closed Access | Times Cited: 80
BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction
M. Ozan Tezcan, Prakash Ishwar, Janusz Konrad
IEEE Access (2021) Vol. 9, pp. 53849-53860
Open Access | Times Cited: 78
ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos
Yunqi Miao, Jungong Han, Yongsheng Gao, et al.
Pattern Recognition Letters (2019) Vol. 125, pp. 113-118
Closed Access | Times Cited: 55
Background subtraction for moving object detection: explorations of recent developments and challenges
Rudrika Kalsotra, Sakshi Arora
The Visual Computer (2021) Vol. 38, Iss. 12, pp. 4151-4178
Closed Access | Times Cited: 53
Convolutional Neural Networks-An Extensive arena of Deep Learning. A Comprehensive Study
Navdeep Singh, Hiteshwari Sabrol
Archives of Computational Methods in Engineering (2021) Vol. 28, Iss. 7, pp. 4755-4780
Closed Access | Times Cited: 44
A survey of moving object detection methods: A practical perspective
Xinyue Zhao, Guangli Wang, Zaixing He, et al.
Neurocomputing (2022) Vol. 503, pp. 28-48
Closed Access | Times Cited: 30
An Epileptic Seizure Prediction Method Based on CBAM-3D CNN-LSTM Model
Xiang Lu, Anhao Wen, Lei Sun, et al.
IEEE Journal of Translational Engineering in Health and Medicine (2023) Vol. 11, pp. 417-423
Open Access | Times Cited: 21
Dynamic object detection using sparse LiDAR data for autonomous machine driving and road safety applications
Akshay Gupta, Shreyansh Jain, Pushpa Choudhary, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124636-124636
Closed Access | Times Cited: 6
Analytics of Deep Neural Network-Based Background Subtraction
Tsubasa Minematsu, Atsushi Shimada, Hideaki Uchiyama, et al.
Journal of Imaging (2018) Vol. 4, Iss. 6, pp. 78-78
Open Access | Times Cited: 54
Optical-flow-based framework to boost video object detection performance with object enhancement
Long Fan, Tao Zhang, Wenli Du
Expert Systems with Applications (2020) Vol. 170, pp. 114544-114544
Closed Access | Times Cited: 43
VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction
Hanling Wang, Qing Li, Heyang Sun, et al.
IEEE Journal on Selected Areas in Communications (2022) Vol. 41, Iss. 1, pp. 90-106
Open Access | Times Cited: 26
Encoder and decoder network with ResNet-50 and global average feature pooling for local change detection
Manoj Kumar Panda, Akhilesh Sharma, Vatsalya Bajpai, et al.
Computer Vision and Image Understanding (2022) Vol. 222, pp. 103501-103501
Closed Access | Times Cited: 23
ZBS: Zero-Shot Background Subtraction via Instance-Level Background Modeling and Foreground Selection
Yongqi An, Xu Zhao, Changyuan Yu, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
Open Access | Times Cited: 13
Statistical Modeling of Deep Features to Reduce False Alarms in Video Change Detection
Xavier Bou, Aitor Artola, Thibaud Ehret, et al.
Journal of Mathematical Imaging and Vision (2025) Vol. 67, Iss. 2
Open Access
Moving object detection using convolutional neural networks (CNN) in comparison with gaussian mixture model (GMM) to measure F-score
M. Monika, Kirupa Ganapathy, R. Puviarasi
AIP conference proceedings (2025) Vol. 3270, pp. 020002-020002
Closed Access
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