An Open-Source Platform for Human Pose Estimation and Tracking Using a Heterogeneous Multi-Sensor System

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-03, Vol.21 (7), p.2340
Main Authors: Patil, Ashok Kumar, Balasubramanyam, Adithya, Ryu, Jae Yeong, Chakravarthi, Bharatesh, Chai, Young Ho
Format: Article
Language:English
Subjects:
Autonomous vehicles
Avatars
detection
heterogeneous sensor
Human body
Human mechanics
Human motion
human pose estimation
Humans
Kinematics
Lasers
Motion
Motion capture
Motion perception
multi-sensor
Neural networks
Open source software
Optimization techniques
Orientation
Parameter estimation
Pose estimation
Position sensing
Principal components analysis
Real time
Robotics
Robots
sensor fusion
Sensors
tracking
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21072340