Fusion of mmWave radar data with image sensors for enhanced vision systems

The integration of millimeter-wave (mmWave) radar with image sensors is gaining significant traction in applications requiring robust perception, including security cameras, automotive safety, and smart surveillance. While image sensors provide rich visual data, they suffer from limitations in poor lighting and occlusion scenarios. Conversely, mmWave radar excels in detecting motion, depth, and presence, irrespective of lighting conditions. This review explores methodologies for fusing mmWave radar data with video streams, discusses signal processing techniques, and presents comparative analyses of different fusion approaches. We also evaluate real-world implementations and benchmark datasets to highlight the efficacy of sensor fusion in improving detection accuracy, privacy preservation, and environmental adaptability.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-30
DOI
https://doi.org/10.5281/zenodo.16993977
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

Fusion of mmWave radar data with image sensors for enhanced vision systems

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Fusion of mmWave radar data with image sensors for enhanced vision systems

article en

Abstract

The integration of millimeter-wave (mmWave) radar with image sensors is gaining significant traction in applications requiring robust perception, including security cameras, automotive safety, and smart surveillance. While image sensors provide rich visual data, they suffer from limitations in poor lighting and occlusion scenarios. Conversely, mmWave radar excels in detecting motion, depth, and presence, irrespective of lighting conditions. This review explores methodologies for fusing mmWave radar data with video streams, discusses signal processing techniques, and presents comparative analyses of different fusion approaches. We also evaluate real-world implementations and benchmark datasets to highlight the efficacy of sensor fusion in improving detection accuracy, privacy preservation, and environmental adaptability.

Zenodo (CERN European Organization for Nuclear Research)
Software Engineering Institute (US)
Openalex Percentile: Top 60%
Geochemistry and Geologic Mapping
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