The following film shows how an object is identified using a model stored in the JANUS model database. The method is similar to the one described in A model based approach to recognition and measurement of partially hidden objects in complex scenes.

In the first phase, the contours of the interesting objects are detected. This is done by a grid search over the picture. Afterwards a flood fill method is applied to get contours of the objects.
In the next phase, a model based fitting is applied to to selected objects parameters (distance, orientation, ...).

As a result of this, one gets classification and a scale model of the object, this includes position and orientation in the camera coordinate system.

To enable the observation everything is much slower than in reality. The methods described in A model based approach to recognition and measurement of partially hidden objects in complex scenes have been further improved, so that we achieve 1-3 seconds on a single PC out of our PC cluster. This time is still quite long, but there is a rich set of promising heuristics, which we have not yet included into the speed up process. Once a good first hypothesis has been found, the fine iteration is very fast (0.05 sec). In the video this results in a very fast fitting at the end of the iteration. Therefore tracking of an already recognized but moving object should be possible in real time.