Situation recognition is an important problem to solve for introducing new capabilities in surveillance applications. It is concerned with recognizing a priori defined situations of interest, which are characterized as being of temporal and concurrent nature. The purpose is to aid decision makers with focusing on information that is known to likely be important for them, given their goals. Besides the two most important problems: knowing what to recognize and being able to recognize it, there are three main problems coupled to real time recognition of situations. Computational complexity — we need to process data and information within bounded time. Tractability — human operators must be able to easily understand what is being modelled. Expressability — we must be able to express situations at suitable levels of abstraction. In this paper we attempt to lower the computational complexity of a Petri net based approach for situation.