A survey of moving object detection methods: A practical perspective
X Zhao, G Wang, Z He, H Jiang - Neurocomputing, 2022 - Elsevier
Moving object detection is the foundation of research in many computer vision fields. In
recent decades, a number of detection methods have been proposed. Relevant surveys …
recent decades, a number of detection methods have been proposed. Relevant surveys …
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 …
Weakly supervised realtime dynamic background subtraction
F Bahri, N Ray - arXiv preprint arXiv:2303.02857, 2023 - arxiv.org
Background subtraction is a fundamental task in computer vision with numerous real-world
applications, ranging from object tracking to video surveillance. Dynamic backgrounds …
applications, ranging from object tracking to video surveillance. Dynamic backgrounds …
Dynamic background subtraction by generative neural networks
F Bahri, N Ray - 2022 18th IEEE International Conference on …, 2022 - ieeexplore.ieee.org
Background subtraction is a significant task in computer vision and an essential step for
many real world applications. One of the challenges for background subtraction methods is …
many real world applications. One of the challenges for background subtraction methods is …
A ResNet-101 deep learning framework induced transfer learning strategy for moving object detection
Background subtraction is a crucial stage in many visual surveillance systems. The prime
objective of any such system is to detect moving objects such that the system could be …
objective of any such system is to detect moving objects such that the system could be …
Moving object detection via feature extraction and classification
Y Li - Open Computer Science, 2024 - degruyter.com
Foreground segmentation (FS) plays a fundamental and important role in computer vision,
but it remains a challenging task in dynamic backgrounds. The supervised method has …
but it remains a challenging task in dynamic backgrounds. The supervised method has …
Weakly Supervised Foreground Object Detection Network Using Background Model Image
In visual surveillance, deep learning-based foreground object detection algorithms are
superior to classical background subtraction (BGS)-based algorithms. However, deep …
superior to classical background subtraction (BGS)-based algorithms. However, deep …
Background initialization in video data using singular value decomposition and robust principal component analysis
Background initialization is used in video processing applications to extract a scene without
the foreground scene. In recent times, the issue of background initialization has gained …
the foreground scene. In recent times, the issue of background initialization has gained …
Face Recognition Using The Subspace and Deep Learning Algorithms For Cases of Sufficient and Insufficient Data
S Keser - Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 2024 - dergipark.org.tr
In face recognition, the distance criterion significantly influences the recognition rate.
Misclassified test signals can be accurately reassigned to the correct class using various …
Misclassified test signals can be accurately reassigned to the correct class using various …
Moving Object Detection for Thermal Video Using Encoder-Decoder Type Deep Learning Framework
Background subtraction (BGS) is an important stage in many thermal surveillance systems.
However, the foremost objective of any such system is to detect local changes, and the …
However, the foremost objective of any such system is to detect local changes, and the …