Multiple Hypothesis Tracking of Clusters of People

Mobile robots operating in populated environments typically can improve their service and navigation behavior when they know where people are in their vicinity and in which direction they are heading. In this paper we present an algorithm for tracking clusters of people using multiple hypothesis tracking (MHT). The motivation for our approach is that tracking clusters of objects instead of the individual objects enhances the reliability and robustness of the tracking especially when the objects move in groups. To efficiently keep track of multiple objects and clusters, our approach uses MHT in combination with Murty´s algorithm. The set of hypothesis for each iteration is constructed in two consecutive steps: one for solving the data association problem, taking also into account the frequent occlusions between the objects, and the second one for considering the joining of different clusters. Our approach has been implemented and tested on a real robot and in a typical hallway environment. Experimental results demonstrate that our approach can robustly deal with several groups of people and is able to reliably manage the splits and joins of clusters