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

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

Visual surveillance requires robust detection of foreground objects under challenging environments of abrupt lighting variation, stationary foreground objects, dynamic background objects, and severe weather conditions. Most classical algorithms leverage background model images produced by statistica...

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Bibliographic Details
Main Authors: Jae-Yeul Kim, Jong-Eun Ha
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Deep learning
foreground object detection
spatiotemporal information
visual surveillance
Online Access:https://ieeexplore.ieee.org/document/10371296/

Internet

https://ieeexplore.ieee.org/document/10371296/

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