Optical Motion Capture:
Theory and Implementation
Tutorial XVIII Brazilian Symposium on
Computer Graphics and Image Processing

Gutemberg Guerra-Filho
Computer Vision Laboratory
Center for Automation Research
University of Maryland
College Park, MD 20742-3275
guerra@cs.umd.edu

Abstract

Motion capture is the process of recording real life movement of a subject as sequences of Cartesian coordinates in 3D space. Optical motion capture (OMC) uses cameras to reconstruct the body posture of the performer. One approach employs a set of multiple synchronized cameras to capture markers placed in strategic locations on the body. A motion capture system has applications in computer graphics for character animation, in virtual reality for human control-interface, and in video games for realistic simulation of human motion. In this tutorial, we discuss the theoretical and empirical aspects of an optical motion capture system. Basically, for a motion capture system implementation, the resources required consist of a number of synchronized cameras, an image acquisition system, a capturing area, and a special suit with markers. The locations of the markers on the suit are designed such that the required body parts (e.g. joints) are covered. We present our motion capture system using a framework that identifies different sub-problems to be solved in a modular way. Therefore, we propose a Matlab( toolbox for Optical Motion Capture where each module version may be implemented in order
to consider different constraints. The sub-problems involved in OMC are initialization, marker detection, spatial correspondence, temporal correspondence, and post-processing. In this tutorial, we discuss the theory involved in each sub-problem and the corresponding novel techniques used in the current implementation. The initialization consists in setting up an anthropomorphic human model and in the
computation of intrinsic and extrinsic camera calibration. Marker detection involves finding the 2D pixel coordinates of markers in the images. The spatial correspondence problem consists in finding pairs of detected markers in different images captured at the same time with different viewpoints such that each pair corresponds to the projections of the same scene point. Given camera calibration and the spatial matching, the 3D reconstruction of markers (translational
data) is achieved by triangulating the various camera views. The temporal correspondence problem (tracking) involves matching two clouds of 3D points representing detected markers at two consecutive frames, respectively. The temporal correspondence module builds a track for each marker where the marker's 3D coordinates are concatenated according to time. Post-processing consists in labeling each track with a marker code, filling track gaps caused by occlusions, correcting possible gross errors, filtering or smoothing noise, and interpolating data along time. Other important techniques used to improve consistency in the motion data are volumetric reconstruction, inverse kinematics, and inverse dynamics. Once the translational data is processed, a hierarchical human model may be used to compute rotational data (joint angles). We consider standard data formats available for motion capture data (e.g. bvh, acclaim) and cover topics related to editing and manipulation of motion data.

Further information: http://www.cs.umd.edu/~guerra/OptMoCap.html