An End to End Encoder-Decoder Network with Multi-scale Feature Pulling for Detecting Local Changes From Video Scene
Author:
Affiliation:
1. GIET University,Gunupur,Odisha,India,765022
2. Indian Institute of Technology Jammu,Jammu & Kashmir,India,181221
3. Laboratoire MIA, La Rochelle Université,La Rochelle,France
4. National Institute of Technology,Goa,India,403401
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9958946/9959140/09959141.pdf?arnumber=9959141
Reference27 articles.
1. An end-to-end deep learning approach for simultaneous background modeling and subtraction;mondejar-guerra;British Machine Vision Conference,2019
2. Deep Adversarial Network for Scene Independent Moving Object Segmentation
3. Motion U-Net:multi-cue encoder-decoder network for motion segmentation;rahmon;International Conference on Pattern Recognition,2021
4. Spatio-contextual Gaussian mixture model for local change detection in underwater video
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2. An Improved VGG-19 Network Induced Enhanced Feature Pooling for Precise Moving Object Detection in Complex Video Scenes;IEEE Access;2024
3. A Multi-Scale Contrast Preserving Encoder-Decoder Architecture for Local Change Detection From Thermal Video Scenes;IEEE Transactions on Information Forensics and Security;2024
4. FPPNet: A Fixed-Perspective-Perception Module for Small Object Detection Based on Background Difference;IEEE Sensors Journal;2023-05-15
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