Classifying humans by movement
Planning an appropriate path around people is only part of the story. Prior research indicates that the speed and direction of movement, both of people and of the robot, are important factors in human-robot interaction. We need to investigate both the detection and classification of the movements of people.
The packages we use for people detection also manage people tracking, in turn populating data structures of people and their positions. Therefore our goal is to classify the behavior of people around the robot based on their movement. Our aim is to determine what level of classification is important to the range of social tasks the robot is attempting. For example, the work of Kanda et al. determines which people are best to approach to present promotional material. Rather than such task-specific classification, we propose a higher-level classification using labels such as “hurrying”, “standing”, “sitting”, and “meandering”, in conjunction with gross level directional labels, such as “toward”, “away”, and “crossing”. It is our contention that these labels on human movement will be sufficient for higher level processes to make a determination of the best people to approach, or people who are likely to approach the robot themselves, such that the robot can prepare for such an event by turning toward the person, for instance.
For in-lab scenarios, we can cross-reference models of detection of people with our motion capture data, as ground truth data.