Towards a universal tracking database

In moving object databases, authors usually assume that number and position of objects to be processed are always known in advance. Detecting an unknown moving object and pursuing its movement, however, is usually left to tracking algorithms resting outside the database. Trackers are complex software systems which process sensor data and application-specific context information in order to detect, classify, monitor and predict the course of moving objects. As there are no universal software tools for realizing a tracker, such systems are usually hand-coded from scratch for each tracking application. In this paper we present a way how to implement a framework for implementing universal trackers inside a database. As a use case, we consider the well-known probabilistic multiple hypothesis tracking approach (PMHT) and the interacting multiple model filter (IMM) for realizing typical tracking tasks. We show that incremental view maintenance techniques and Bregman Ball trees are well-suited for efficiently implementing state-of-the-art trackers for processing streams of radar data.