Stars
The official implementation of HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar
This project classifies two groups (walking and running) from the FMCW dataset. Different deep learning models (LSTM, GRU) will be used.
Detect (classify and localize) people, pets and objects using millimeter-wave radar.
Using MMWave Radar to predict people's position
The ros package is to interface the AWR1843BOOST EVM with the ROS environment. It returns the data in the PoinCloud2 format along with the other information of the detected objects including veloci…
Codes and template data for paper "Experiments with mmWave Automotive Radar Test-bed"
Radar-based classification of ground moving targetsrelies on Doppler information. Therefore, the classification be-tween humans and animals is a challenging task due to theirsimilar Doppler signatu…
A dataset for the raw ADC data of 2D-MIMO MMWave Radar for carry object detection.
An open source library for interacting with and processing radar data, specialized for MIMO mmWave radars
mPose3D, a mmWave-based 3D human pose estimation model.
fraunhoferhhi / racpit
Forked from dxyang/StyleTransferCode and additional information to our paper "RACPIT: Improving Radar Human Activity Classification Using Synthetic Data with Image Transformation"
[mmWave based fmcw radar design files] based on AWR1843 chip operating at 76-GHz to 81-GHz.
FMCW Radar 77GHz - Range-Velocity and displacement simualtion - 2D FFT process to generate Range Doppler Maps - CFAR Processing to display the target
Generate FMCW Waveform and Detect Target by filtering the Clutter
Classification of Human Movement using mmWave FMCW Radar Micro-Doppler Signature
Sign Language Gesture Recognition From Video Sequences Using RNN And CNN