MSF-NET: Foreground Objects Detection With Fusion of Motion and Semantic Features-Reference-Cited by-同舟云学术

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MSF-NET: Foreground Objects Detection With Fusion of Motion and Semantic Features

Published:2023 Issue: Volume:11 Page:145551-145565
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kim Jae-Yeul¹^ORCID,Ha Jong-Eun²^ORCID

Affiliation:

1. Graduate School of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea

2. Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology, Seoul, South Korea

Funder

Korea Government (MSIT)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10371296.pdf?arnumber=10371296

Reference65 articles.

1. CDnet 2014: An Expanded Change Detection Benchmark Dataset

2. Learning multi-scale features for foreground segmentation

3. Deep background subtraction with scene-specific convolutional neural networks

4. Scene Independency Matters: An Empirical Study of Scene Dependent and Scene Independent Evaluation for CNN-Based Change Detection

5. Task-based parameter isolation for foreground segmentation without catastrophic forgetting using multi-scale region and edges fusion network

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1. A small moving object detection method based on attention mechanism and location analysis from infrared images;International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024);2024-06-13

2. CTFU-Net:CNN-Transformer Fusion U-shaped Network for Moving Object Detection;2024 3rd International Conference on Image Processing and Media Computing (ICIPMC);2024-05-17

3. Learning Temporal Distribution and Spatial Correlation Toward Universal Moving Object Segmentation;IEEE Transactions on Image Processing;2024

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