Tracking groups of people with a multi-model hypothesis tracker

Abstract— People in densely populated environments typically form groups that split and merge. In this paper we track groups of people so as to reflect this formation process and gain efficiency in situations where maintaining the state of individual people would be intractable. We pose the group tracking problem as a recursive multi-hypothesis model selection problem in which we hypothesize over both, the partitioning of tracks into groups (models) and the association of observations to tracks (assignments). Model hypotheses that include split, merge, and continuation events are first generated in a datadriven manner and then validated by means of the assignment probabilities conditioned on the respective model. Observations are found by clustering points from a laser range finder given a background model and associated to existing group tracks using the minimum average Hausdorff distance. Experiments with a stationary and a moving platform show that, in populated environments, ...

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Boris Lau, Kai Oliver Arras, Wolfram Burgard
• Keywords: Densely Populated, ICRA 2009, Model Selection Problem, Populated Environments, Robotics