Text-to-logic conversion is studied in a system approach which extracts a conceptual representation of temporal developments within a road traffic scene recorded by a video camera. A Fuzzy Metric Temporal Horn Logic (FMTHL) facilitates a schematic representation of road vehicle behavior at intersections. Geometric results of a model-based vehicle detection and tracking subsystem are used to interpret this generic conceptual FMTHL representation in order to obtain a conceptual description of the specific developments in the recorded traffic scene.

The algorithmic generation of textual descriptions of real world image sequences requires conceptual knowledge. The algorithmic generation of synthetic image sequences from textual descriptions requires conceptual knowledge, too. An explicit representation formalism for behavioral knowledge based on formal logic is presented which can be utilized in both tasks -- Understanding and Creation of video sequences.

The algorithmic generation of textual descriptions of image sequences requires conceptual knowledge. In our case, a stationary camera recorded image sequences of road tra#c scenes. The necessary conceptual knowledge has been provided in the form of a so-called Situation Graph Tree (SGT). Other endeavors such as the generation of a synthetic image sequence from a textual description or the transformation of machine vision results for use in a driver assistance system could profit from the exploitation of the same conceptual knowledge, but more in a planning (pre-scriptive) rather than a de-scriptive context.

The textual description of video sequences exploits conceptual knowledge about the behavior of depicted agents. An explicit representation of such behavioral knowledge facilitates not only the textual description of video evaluation results, but can also be used for the inverse task of generating synthetic image sequences from textual descriptions of dynamic scenes. Moreover, it is shown here that the behavioral knowledge representation within a cognitive vision system can be exploited even for prediction of movements of visible agents, thereby improving the overall performance of a cognitive vision system.