Parallel Detection of Conversational Groups of Free-Standing People and Tracking of their Lower-Body Orientation

Abstract

Appropriate robot behavior in public, open spaces cannot occur without the ability to automatically detect conversational groups of free-standing people. To this end, we propose an alternating optimization procedure that estimates lower body orientations and detects groups of interacting people. The first task is achieved by tracking the direction of the lower body of the people in the scene based on their position, their head orientation, the location of objects of interest in their vicinity, and their groups. For the second task, we propose a new group detection algorithm based on F-formation detection. This method can reason about lower body orientation distributions, and generates soft group assignments for the orientation trackers. We evaluate the proposed approach on a publicly available dataset, and show that it can improve state-of- the art detection of non-interacting people without sacrificing group detection accuracy. This is particularly useful for robots since it provides more opportunities for starting interactions and can help estimate disengagement.