Research on information fusion and situation management within the military domain, is often focused on data-driven
approaches for aiding decision makers in achieving situation awareness. We have in a companion paper identified
situation recognition as an important topic for further studies on knowledge-driven approaches. When developing new
algorithms it is of utmost importance to have data for studying the problem at hand (as well as for evaluation purposes).
This often become a problem within the military domain as there is a high level of secrecy, resulting in a lack of data,
and instead one often needs to resort to artificial data. Many tools and simulation environments can be used for
constructing scenarios in virtual worlds. Most of these are however data-centered, that is, their purpose is to simulate the
real-world as accurately as possible, in contrast to simulating complex scenarios. In high-level information fusion we can
however often assume that lower-level problems have already been solved - thus the separation of abstraction - and we
should instead focus on solving problems concerning complex relationships, i.e. situations and threats. In this paper we
discuss requirements that research on situation recognition puts on simulation tools. Based on these requirements we
present a component-based simulator for quickly adapting the simulation environment to the needs of the research
problem at hand. This is achieved by defining new components that define behaviors of entities in the simulated world.
The process of tracking and identifying developing situations is an ability of importance within the surveillance domain.
We refer to this as situation recognition and believe that it can enhance situation awareness for decision makers.
Situation recognition requires that many subproblems are solved. For instance, we need to establish which situations are
interesting, how to represent these situations, and which inferable events and states that can be used for representing
them. We also need to know how to track and identify situations and how to determine the correlation between present
information about situations with knowledge. For some of these subproblems, data-driven approaches are suitable, whilst
knowledge-driven approaches are more suitable for others. In this paper we discuss our current research efforts and goals
concerning template-based situation recognition. We provide a categorization of approaches for situation recognition
together with a formalization of the template-based situation recognition problem. We also discuss this formalization in
the light of a pick-pocket scenario. Finally, we discuss future directions for our research on situation recognition. We
conclude that situation recognition is an important problem to look into for enhancing the overall situation awareness of