An open-source platform for human pose estimation and tracking using a heterogeneous multi-sensor systemopen access
- Authors
- Patil, A.K.; Balasubramanyam, A.; Ryu, J.Y.; Chakravarthi, B.; Chai, Y.H.
- Issue Date
- Apr-2021
- Publisher
- MDPI AG
- Keywords
- Detection; Heterogeneous sensor; Human pose estimation; Inertial sensor; Lidar sensor; Multi-sensor; Sensor fusion; Tracking
- Citation
- Sensors, v.21, no.7
- Journal Title
- Sensors
- Volume
- 21
- Number
- 7
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47711
- DOI
- 10.3390/s21072340
- ISSN
- 1424-8220
1424-3210
- Abstract
- Human pose estimation and tracking in real-time from multi-sensor systems is essential for many applications. Combining multiple heterogeneous sensors increases opportunities to improve human motion tracking. Using only a single sensor type, e.g., inertial sensors, human pose estimation accuracy is affected by sensor drift over longer periods. This paper proposes a human motion tracking system using lidar and inertial sensors to estimate 3D human pose in real-time. Human motion tracking includes human detection and estimation of height, skeletal parameters, position, and orientation by fusing lidar and inertial sensor data. Finally, the estimated data are reconstructed on a virtual 3D avatar. The proposed human pose tracking system was developed using open-source platform APIs. Experimental results verified the proposed human position tracking accuracy in real-time and were in good agreement with current multi-sensor systems. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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