Automated Analysis of Pedestrian Group Behavior in Urban Settings

Movement trajectory of pedestrians, when tracked from video data, enables the automated analysis of individual's walking behavior. For example, speed preferences and walking strategies are typical behavior characteristics that benefit from this analysis. When pedestrians are walking in a group, they tend to adjust their speed and direction accordingly, while maintaining interpersonal distances. The adopted walking strategy leads to a coupling in their movement behavior. Such commonality, if considered, permits the discrimination between pedestrian groups and the distinction of pedestrians in different groups. Those are important factors when tracking a group of pedestrians or counting pedestrians in the crowd. The objective of this paper is localizing pedestrians in small groups using automated computer vision tracking. This paper describes the following tasks. First, to identify possible commonality in walking behavior between nearby pedestrians. This step is realized by proposing a new structural similarity measure of pedestrians' movement. Second, to provide a method for counting pedestrians in groups. A classification procedure accomplishes this task based on spatio-temporal criteria and the introduced movement similarity measure. Third, to show the feasibility of the method on a pedestrian group study from video data collected at a moderately dense pedestrian crosswalk in Vancouver, British Columbia. A validation of the group size classification demonstrated an accuracy of up to 77%. This paper enables a faster stream for comprehensive pedestrian data collection. Also, the new measure for group behavior can be useful when studying the mechanism of group formation and collision avoidance.