Patil AK, Balasubramanyam A, Ryu JY, Chakravarthi B, Chai YH. An Open-Source Platform for Human Pose Estimation and Tracking Using a Heterogeneous Multi-Sensor System.
SENSORS 2021;
21:s21072340. [PMID:
33801716 PMCID:
PMC8037698 DOI:
10.3390/s21072340]
[Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 03/24/2021] [Accepted: 03/25/2021] [Indexed: 11/24/2022]
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.
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