TY - GEN
T1 - M4esh
T2 - 20th ACM Conference on Embedded Networked Sensor Systems, SenSys 2022
AU - Xue, Hongfei
AU - Cao, Qiming
AU - Ju, Yan
AU - Hu, Haochen
AU - Wang, Haoyu
AU - Zhang, Aidong
AU - Su, Lu
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2023/1/24
Y1 - 2023/1/24
N2 - The recent proliferation of various wireless sensing systems and applications demonstrates the advantages of radio frequency (RF) signals over traditional camera-based solutions that are faced with various challenges, such as occlusions and poor lighting conditions. Towards the ultimate goal of imaging human body using RF signals, researchers have been exploring the possibility of constructing the human mesh, a structure capturing not only the pose but also the shape of the human body, from RF signals. In this paper, we introduce M4esh, a novel system that utilizes commercial millimeter wave (mmWave) radar for multi-subject 3D human mesh construction. Our M4esh system can detect and track the subjects on a 2D energy map by predicting the subject bounding boxes on the map, and tackle the subjects' mutual occlusion through utilizing the location, velocity and size information of the subjects' bounding boxes from the previous frames as a clue to estimate the bounding box in the current frame. Through extensive experiments on a real-world COTS millimeter-wave testbed, we show that our proposed M4esh system can accurately localize the subjects and generate their human meshes, which demonstrate the superior effectiveness of the proposed M4esh system.
AB - The recent proliferation of various wireless sensing systems and applications demonstrates the advantages of radio frequency (RF) signals over traditional camera-based solutions that are faced with various challenges, such as occlusions and poor lighting conditions. Towards the ultimate goal of imaging human body using RF signals, researchers have been exploring the possibility of constructing the human mesh, a structure capturing not only the pose but also the shape of the human body, from RF signals. In this paper, we introduce M4esh, a novel system that utilizes commercial millimeter wave (mmWave) radar for multi-subject 3D human mesh construction. Our M4esh system can detect and track the subjects on a 2D energy map by predicting the subject bounding boxes on the map, and tackle the subjects' mutual occlusion through utilizing the location, velocity and size information of the subjects' bounding boxes from the previous frames as a clue to estimate the bounding box in the current frame. Through extensive experiments on a real-world COTS millimeter-wave testbed, we show that our proposed M4esh system can accurately localize the subjects and generate their human meshes, which demonstrate the superior effectiveness of the proposed M4esh system.
KW - deep learning
KW - human mesh estimation
KW - millimeter wave
KW - multiple subjects
KW - point cloud
KW - wireless sensing
UR - https://www.scopus.com/pages/publications/85147549641
U2 - 10.1145/3560905.3568545
DO - 10.1145/3560905.3568545
M3 - Conference contribution
AN - SCOPUS:85147549641
T3 - SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
SP - 391
EP - 406
BT - SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
PB - Association for Computing Machinery, Inc
Y2 - 6 November 2022 through 9 November 2022
ER -