Timezone: »
Multi-modal 3D Human Pose Estimation using mmWave, RGB-D, and Inertial Sensors
Sizhe An · Yin Li · Umit Ogras
Event URL: https://openreview.net/forum?id=6-SdB-dzXVm »
The ability to estimate 3D human body pose and movement, also known as human pose estimation~(HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few dataset exploits multiple modalities and focuses on home-based health monitoring. To bridge this gap, we present human pose estimation using multiple modalities with an in-house dataset. We perform extensive experiments and delineate the strength of each modality.
Author Information
Sizhe An (University of Wisconsin-Madison)
Yin Li (University of Wisconsin-Madison)
Umit Ogras (University of Wisconsin - Madison)
More from the Same Authors
-
2022 : tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices »
Toygun Basaklar · Yigit Tuncel · Umit Ogras -
2022 : tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices »
Toygun Basaklar · Yigit Tuncel · Umit Ogras -
2022 : PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm »
Toygun Basaklar · Suat Gumussoy · Umit Ogras -
2022 Poster: mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors »
Sizhe An · Yin Li · Umit Ogras -
2018 Poster: Beyond Grids: Learning Graph Representations for Visual Recognition »
Yin Li · Abhinav Gupta