Being determined by human social behaviour, pedestrian group dynamics may depend on “intrinsic properties” such as the purpose of the pedestrians, their personal relation, gender, age, and body size. In this work we investigate the dynamical properties of pedestrian dyads (distance, spatial formation and velocity) by analysing a large data set of automatically tracked pedestrian trajectories in an unconstrained “ecological” setting (a shopping mall), whose apparent physical and social group properties have been analysed by three different human coders. We observed that females walk slower and closer than males, that workers walk faster, at a larger distance and more abreast than leisure oriented people, and that inter-group relation has a strong effect on group structure, with couples walking very close and abreast, colleagues walking at a larger distance, and friends walking more abreast than family members. Pedestrian height (obtained automatically through our tracking system) influences velocity and abreast distance, both growing functions of the average group height. Results regarding pedestrian age show that elderly people walk slowly, while active age adults walk at the maximum velocity. Groups with children have a strong tendency to walk in a non-abreast formation, with a large distance (despite a low abreast distance). A cross-analysis of the interplay between these intrinsic features, taking in account also the effect of an “extrinsic property” such as crowd density, confirms these major results but reveals also a richer structure. An interesting and unexpected result, for example, is that the velocity of groups with children increases with density, at least in the low-medium density range found under normal conditions in shopping malls. Children also appear to behave differently according to the gender of the parent.

We study the dependence on crowd density of the spatial size, configuration, and velocity of pedestrian social groups. We find that, in the investigated density range, the extension of pedestrian groups in the direction orthogonal to that of motion decreases linearly with the pedestrian density around them, both for two- and three-person groups. Furthermore, we observe that at all densities, three-person groups walk slower than two-person groups, and the latter are slower than individual pedestrians, the differences in velocities being weakly affected by density. Finally, we observe that three-person groups walk in a V-shaped formation regardless of density, with a distance between the pedestrians in the front and back again almost independent of density, although the configuration appears to be less stable at higher densities. These findings may facilitate the development of more realistic crowd dynamics models and simulators.

A method for tracking the position, orientation, and height of persons in large public environments is presented. Such a piece of information is known to be useful both for understanding their actions, as well as for applications such as human-robot interaction. We use multiple 3-D range sensors, which are mounted above human height to have less occlusion between persons. A computationally simple-tracking method is proposed that works on single sensor data and combines multiple sensors so that large areas can be covered with a minimum number of sensors. Moreover, it can work with different sensor types and is robust to the imperfect sensor measurements; therefore, it is possible to combine currently available 3-D range sensor solutions to achieve tracking in wide public spaces. The method was implemented in a shopping center environment, and it was shown that good tracking performance can be achieved. Index Terms-3-D range sensors, person tracking.

Social robots working in public space often stimulate children's curiosity. However, sometimes children also show abusive behavior toward robots. In our case studies, we observed in many cases that children persistently obstruct the robot's activity. Some actually abused the robot by saying bad things, and at times even kicking or punching the robot. We developed a statistical model of occurrence of children's abuse. Using this model together with a simulator of pedestrian behavior, we enabled the robot to predict the possibility of an abuse situation and escape before it happens. We demonstrated that with the model the robot successfully lowered the occurrence of abuse in a real shopping mall.

No abstract