hide
Free keywords:
-
Abstract:
In this chapter, we present a model-free pose estimation algorithm to estimate
the relative pose of a rigid object. In the context of human motion, a rigid
object can be either a limb, the head, or the back. In most pose estimation
algorithms, the object
of interest covers a large image area. We focus on pose estimation of objects
covering a field of view of less than
5$^\circ$\ by 5$^\circ$\ using stereo vision.
With this new algorithm suitable for small objects, we
investigate the effect of the object size on the pose accuracy.
In addition, we introduce an object tracking technique that is insensitive
to partial occlusion. We are particularly interested in human motion
in this context focusing on crash test dummies.
The main application for this method is the analysis of crash video sequences.
For a human motion capture system, a connection of the various limbs can be
done in an additional step.
The ultimate goal is to fully obtain the motion of crash test dummies
in a vehicle crash. This would give information on which body part is
exposed to what kind of forces and rotational forces could be
determined as well. Knowing all this, car manufacturers can optimize
the passive safety components to reduce forces on the dummy and
ultimately on the real vehicle passengers.
Since
camera images for crash videos contain the whole crash vehicle, the size of the
crash
test dummies is relatively small in our experiments.
For these experiments, mostly high-speed cameras with high resolution
are used. However, the method described here
easily extends to real-time robotics
applications with smaller VGA-size images,
where relative pose estimation is needed, {e.g.}\ for manipulator control.
