As a person walks or an automobile drives through a scene, it presents differently at each frame. We attempt to use the invariance shown within the tracks to give us information about how to cluster the represenations. Using those automatically generated clusters, we have taken all the activity in a scene for a particular morning and seperated it into two seperate TrackApplets. While this classification is simple, it can reduce your query domain size by one half in many cases. This example uses the position, movement, and size characteristics of the objects over time to cluster based on the activity of the object.
Every event is shown in the corresponding template.
While this may seem like an obvious break to make, it only made it because very few objects showed both eastbound and westbound characteristics. This is not the case in many scenes. Further breaks break activities down into very tight clusters of activities. If you click on any of the numbers below the tree you will be able to view the activities of the corresponding leaf node in a Java 1.1 Applet. Query-based Retrieval One of the easier methods of interacting with our system is selecting a number of objects which exhibit the characteristics you are interested in and allowing the system to find similar ones.