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We additionally describe a method to detect and handle dynamic objects such as moving persons.
It was found that for simple dynamic objects such as bars and polygon objects, a line changing in angle was the most noticeable emergent feature to show a departure from "normal" state.
When it comes to visual behaviour data, a difference was also found concerning the area of interest the drivers looked at; while the older drivers looked more at lines and markings on the road to position themselves in the traffic, the younger drivers looked more at dynamic objects such as other cars representing a possible threat.
virtual dynamic objects (such as other cars, pedestrians, etc).
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Advantages of SPH for such modelling include automatic representation of the free surface flow behaviour due to the Lagrangian nature of the method, and the ability to incorporate complex and dynamic boundary objects such as gate structures or debris.
The correctness of the modeled background is usually affected by three factors [3]: (1) illumination changes; (2) dynamic backgrounds: some "moving" objects, such as waving trees, fountains, and flickering monitors, are not interested for a vision-based surveillance system; and (3) shadows: foreground objects often cast shadows, which are different from the modeled background.
Standish also states that dynamic pointers that automatically track objects such as agents are important.
Because they typically do not make any distinction between lower-level objects (such as instances of java.util.Date) and architectural relevant objects (such as collections of Customer objects), dynamic diagrams may have thousands of nodes even for small-sized systems [1, 2].
Objects such as these demonstrate more than craftsmanship, however.
Minor objects such as photographs withstand and repay examination.
Ordinary or "vulgar" objects such as tables and ships are not primary objects.
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