Page 70 - Dynamic Vision for Perception and Control of Motion

54 2 Basic Relations: Image Sequences – “the World”

space; this brought about a quantum leap in the performance capabilities of real-
time computer vision. These methods will be discussed for road vehicle applica-
tions in later sections.

2.3.2 Sudden Changes and Discontinuities

The optimal settings of parameters for smooth pursuit lead to unsatisfactory track-
ing performance in case of sudden changes. The onset of a harsh braking maneuver
of a car or a sudden turn may lead to loss of tracking or at least to a strong transient
motion estimated. If the onsets of these discontinuities can be predicted, a switch in
model or tracking parameters at the right moment will yield much better results.
For a bouncing ball, the moment of discontinuity can easily be predicted by the
time of impact on the ground or wall. By just switching the sign of the angle of in-
cidence relative to the normal of the reflecting surface and probably decreasing
speed by some percentage, a new section of a smooth trajectory can be started with
very likely initial conditions. Iteration will settle much sooner on the new, smooth
trajectory arc than by continuing with the old model disregarding the discontinuity
(if this recovers at all).
In road traffic, the compulsory introduction of the braking (stop) lights serves
the same purpose of indicating that there is a sudden change in the underlying be-
havioral mode (deceleration), which can otherwise be noticed only from integrated
variables such as speed and distance. The pitching motion of a car when the brakes
are applied also gives a good indication of a discontinuity in longitudinal motion; it
is, however, much harder to observe than braking lights in a strong red color.

Conclusion:
As a general scheme in vision, it can be concluded that partially smooth sec-
tions and local discontinuities have to be recognized and treated with proper
methods both in the 2-D image plane (object boundaries) and on the time
line (events).

2.4 Spatiotemporal Embedding and First-order
Approximations

After the rather lengthy excursion to object modeling and how to embed temporal
aspects of visual perception into the recursive estimation approach, the overall vi-
sion task will be reconsidered in this section. Figure 2.7 gave a schematic survey of
the way features at the surface of objects in the real 3-D world are transformed into
features in an image by a properly defined sequence of “homogeneous coordinate
transformations” (HCTs). This is easily understood for a static scene.
To understand a dynamically changing scene from an image sequence taken by
a camera on a moving platform, the temporal changes in the arrangements of ob-
jects also have to be grasped by a description of the motion processes involved.