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Found 11 papers, 4 papers with code
MMW-Carry: Enhancing Carry Object Detection through Millimeter-Wave Radar-Camera Fusion
This paper introduces MMW-Carry, a system designed to predict the probability of individuals carrying various objects using millimeter-wave radar signals, complemented by camera input.
Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels
In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers.
Static Background Removal in Vehicular Radar: Filtering in Azimuth-Elevation-Doppler Domain
Our proposed algorithm follows a three-step approach: a) preprocessing of back-scattered received radar signal for 4-dimensional (4D) point clouds generation, b) 3-dimensional (3D) radar ego-motion estimation, and c) notch filter-based background removal in the azimuth-elevation-Doppler domain.
MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification
Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications.
Learning to Detect Open Carry and Concealed Object with 77GHz Radar
Detecting harmful carried objects plays a key role in intelligent surveillance systems and has widespread applications, for example, in airport security.
Perception Through 2D-MIMO FMCW Automotive Radar Under Adverse Weather
Millimeter-wave (mmWave) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) features that require accurate location and Doppler velocity estimates of objects, independent of environmental conditions.
MIMO-SAR: A Hierarchical High-resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving
Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features.
RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition
Millimeter-wave radars are being increasingly integrated into commercial vehicles to support new advanced driver-assistance systems by enabling robust and high-performance object detection, localization, as well as recognition - a key component of new environmental perception.
Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes
Adverse drug events (ADEs) are a serious health problem that can be life-threatening.
RODNet: Radar Object Detection Using Cross-Modal Supervision
Radar is usually more robust than the camera in severe driving scenarios, e. g., weak/strong lighting and bad weather.
Experiments with mmWave Automotive Radar Test-bed
Millimeter-wave (mmW) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) for its ability to provide high accuracy location, velocity, and angle estimates of objects, largely independent of environmental conditions.
