Detection of groups in crowd considering their activity state
In this paper, we focus on the problem of group detection in crowd, which is a task of partitioning a set of pedestrians in a scene into small subsets called groups based on their trajectories. Most of previous methods use only a single model for representing a relationship between trajectories of pedestrians who belong to the same group. However, such relationship would vary depending on the activity state (e.g. walking together, approaching, splitting, and so on) of the group. In this paper, we propose a novel group detection method which can cope with a variation of groups' activity state. The proposed method constructs different models for each activity state in order to appropriately evaluate the relationship of pedestrians' trajectories. In addition, our method regards groups' activity state as hidden variables and estimates their probability distributions, which is used for integrating the constructed models. The proposed method outperforms existing methods in the experiment on the public dataset.