Page 125 - Dynamic Vision for Perception and Control of Motion

3.6 Growth Potential of the Concept, Outlook 109

Fig. 3.31. Quantitative recognition of motion parameters of a human leg while
running: simulation with real image sequence processing (after [Kinzel 1994]).

Figure 3.30. Quantitative recognition of motion parameters of a human
leg while running: simulation with real image sequence processing, after
[Kinzel 1994].

evaluation and tracking. At that time, microprocessor resources were not sufficient
to do this onboard a car in real time (at least a factor of 5 was missing). In the
meantime, computing power has increased by more than two orders of magnitude
per processor, and human gesture recognition has attracted quite a bit of attention.
Also the wide-spread activities in computer animation with humanoid robots, and
especially the demanding challenge of the humanoid robo-cup league have ad-
vanced this field considerably, lately.
From the field last-mentioned and from analysis of sports as well as dancing ac-
tivities there will be a pressure towards automatically recognizing human (-oid)
motion. This field can be considered developing on its own; application within
semi-autonomous road or autonomous ground vehicles will be more or less a side
product. The knowledge base for these application areas of ground vehicles has to
be developed as a specific effort, however. In case of construction sites or accident
areas with human traffic regulation, future (semi-) autonomous vehicles should