To overcome some of these challenges, we have developed an algorithm for tracking individual targets in high density crowd scenes containing hundreds of people at a time. The proposed algorithm is based on the observation that the locomotive behavior of an individual in a crowded scene is a function of collective patterns evolving from the space-time interactions of individuals among themselves and with the layout of the scene. These collective behavioral patterns, therefore, can be included as an auxiliary source of information to constrain the likely locations or paths that can be taken by the target object in the scene. In other words, natural crowd flow and scene constraints influencing the behavior of a person in a dense crowd can be used as priors to impose high-level direction for tracking purposes.

The flow of the crowd and scene structure is incorporated by introducing the concept of floor fields, which model the interactions between individuals and their preferred direction of movement by transforming long ranged forces into local ones. For instance, a long range force that compels an individual in a crowd to move towards the exit door, can be converted into a local force such that it increases the instantaneous probability of a move in that direction. The transition probability of a tracked person then depends on the strength of the floor field in his/her neighborhood. The concept of a floor field is inspired by the field of evacuation dynamics, where floor fields are manually designed to simulate behaviors of pedestrians in panic situations. We compute three floor fields automatically from the visual data: a `Static Floor Field' (SFF), a `Boundary Floor Field' (BFF), and a `Dynamic Floor Field' (DFF).