We propose a method to detect and track interacting people by employing a framework based on a Social Force Model (SFM). The method embeds plausible human behaviors to predict interactions in a crowd by iteratively minimizing the error between predictions and measurements. We model people approaching a group and restrict the group formation based on the relative velocity of candidate group members. The detected groups are then tracked by linking their interaction centers over time using a buffered graph-based tracker. We show how the proposed framework outperforms existing group localization techniques on three publicly available datasets, with improvements of up to 13% on group detection.
Mazzon et al. REF extended the delayed social force model for group detection by defining plausible human behaviors for the localization of group formations.
Detection and tracking of groups in crowd
2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance
2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance
Computer Science