Better models for people tracking

People tracking is a key component for robots operating in populated environments. Previous works have employed different filtering and data association techniques for this purpose that typically rely on a set of generic assumptions on target behavior and detector characteristics. In this paper, we focus on these assumptions rather than the tracking approach itself and show that with informed models, people tracking can be made substantially more accurate without compromising efficiency. Concretely, we present better, human-specific models for the occurrence of new tracks, false alarms, track occlusions, and track deletions. In the experiments with a large-scale outdoor data set collected with a laser range finder, the models and combinations thereof are experimentally compared using a multi-hypothesis baseline tracker and the CLEAR MOT metrics. The results show how some models selectively improve tracking performance at the expense of other measures. The final combination is then able to resolve the trade-offs, leading to a reduction of data association errors by more than a factor of two at the same cost.