The natural motion of the human body is captured and analyzed without attaching markers or straps.
Top left: One of the 8 video streams used for the motion capture process. Captured motion courtesy of Kath Boyer, PhD.
Top right: Visual Hull (green) and overlaid matched model (yellow).
Bottom left: The captured motion can be displayed, re-synthesized or re-targeted automatically in 3D without needing further processing.
Introduction
The purpose of human motion capture systems in biomechanics is to measure the motion of bony segments during various activities. Most current motion capture systems rely on cumbersome skin based markers for tracking. The system being developed by the BioMotion Lab uses only high speed video cameras and does not use skin markers, capturing the subject's motion in a natural and easier way.
Algorithm Steps
The following describes the important steps involved in the algorithm:
1. Data Acquisition
2. Reconstruction of 3D Representation
3. Model Matching
4. Kinematic Extraction
In the accurate analysis of human motion, including both kinematics and kinetics variables, the quality of the kinematic models implemented in markerless motion capture plays a major role. A first approach uses machine learning techniques to perform dimensionality reduction of human shape variability and learns the optimal location of the joint centers with respect to the shape mesh. Morphologic and kinematic models are generated.
The SCAPE database of human shapes developed by Anguelov et al. SIGGRAPH 2005 is used to automatically generate the subject specific model used in the tracking process.
The Markerless Motion Capture System developed at the BioMotion Lab provides the full body calculation of joint angles and joint centers. The system is synchronized with force plate input to allow the calculation of forces and moments at the joints. Biomechanical variables are obtained at a frame rate up to 200 Hz.
The markerless motion capture system developed at the BioMotion Lab focuses on biomechanical applications, spanning from clinical gait analysis to sport performances and injury prevention. Below are some examples of motion capture of sport activities.
When greater accuracy is needed functional methods are applied for a more accurate estimation of the location of the joint centers. Below an example for the hip joint is given. The sequence is tracked first and then the joint center location refined up to a sub-voxel accuracy (i.e. error<1cm).
Although the main focus is biomechanical applications, the markerless system developed at the BioMotion Lab can output in standard BVH format to allow the direct animation of virtual characters in 3D rendering engines.
