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Cappazzo (1984), Heyn et al (1996), Kidder et al (1996), Sampath et al. (1998) and Hansen et al.
(2002)]. The ability to carefully analyze and
characterize a patient's manner and rate of
movement (gait) has greatly aided in the
development of treatment options for the physically
handicapped. Requirements of very precise
measurements, extended workspaces and limited
physical constraints are similar to those faced in
many civil engineering applications. An example of
a human motion study (applied to dance) conducted
using an optical (light)-based approach is shown in
Figure 1.
Vision-Based Systems
Vision-based systems may be classified into either
image- or light-based. Image-based systems in the
context of motion tracking rely on feature detection
between frames of a color texture map. In parallel
with feature detection, image processing and
reconstruction must be conducted, a challenging task
for resolving the tracking to the level of resolution
required for seismic motions.
FIGURE 1. Example of human motion study
(dance) using light-based approach (courtesy of
Motion Analysis).
Early forms of light-based tracking were based on emitter/receiver systems using arrays of light
emitting diodes (LEDs) mounted statically at specific locations in a test space and a CCD camera
(charge-coupled device) responsible for recording the created reference pattern. Given the exact
spacing and position of the LEDs, the relative position of the CCD can then be triangulated. More
recent techniques have removed the need for emitter/receiver pairs by directly analyzing image (video)
data. The most popular approach currently uses reflective markers to identify points of interest within
the environment that should be tracked, significantly reducing the required processing time. In this
case, a range of wavelengths of light are filtered out, thus decreasing the required amount of
information to be collection, allowing higher speeds and resolution to be captured. The CCD cameras
are then used to measure the light intensity at each pixel. A strobe constructed of a cluster of highintensity
LEDs is used on a per camera basis to illuminate the scene with red light. This strobe can
easily illuminate reflective markers at distance between 2-25 meters.
During the data acquisition stage only raw data (images showing the marker position) is acquired on a
per camera basis. Each marker is represented by multiple pixels in the final image. Before the spatial
position can be calculated, the marker "blobs" have to be approximated (by ellipses or circles) and
their respective centroids determined. The position of the corresponding points can then be matched
between image pairs obtained from two cameras and triangulated to obtain their 3D position. The
corresponding markers between two images can be found based on the epipolar constraint, which
states that a point in the first image must lie on the epipolar line in the second. (This reduces the
matching problem from an area to a line segment). Given the projection of a marker into one image
plane at p l and into another image plane at p 2 , the epipolar constraint is expressed by:
•F- Pl =0 (1)