Motion prediction and interaction localisation of people in crowds

Abstract

The ability to analyse and predict the movement of people in crowded scenarios can be of fundamental importance for tracking across multiple cameras and interaction localisation. In this thesis, we propose a person re-identification method that takes into account the spatial location of cameras using a plan of the locale and the potential paths people can follow in the unobserved areas. These potential paths are generated using two models. In the first, people’s trajectories are constrained to pass through a set of areas of interest (landmarks) in the site. In the second we integrate a goal-driven approach to the Social Force Model (SFM), initially introduced for crowd simulation. SFM models the desire of people to reach specific interest points (goals) in a site, such as exits, shops, seats and meeting points while avoiding walls and barriers. Trajectory propagation creates the possible re-identification candidates, on which association of people across cameras is performed using spatial location of the candidates and appearance features extracted around a person’s head. We validate the proposed method in a challenging scenario from London Gatwick airport and compare it to state-of-the-art person re-identification methods. Moreover, we perform detection and tracking of interacting people in a framework based on SFM that analyses people’s trajectories. The method embeds plausible human behaviours to predict interactions in a crowd by iteratively minimising 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 centres of interaction over time using a buffered graph-based tracker. We show how the proposed framework outperforms existing group localisation techniques on three publicly available datasets.