Multi Person Wandering Visual Focus of Attention

Estimating the wandering visual focus of attention (WVFOA) for multiple people is an important problem with many applications in human behavior understanding. One such application, addressed in this paper, monitors the attention of passers-by to outdoor advertisements. To solve the WVFOA problem, we propose a multi-person tracking approach based on a hybrid Dynamic Bayesian Network that simultaneously infers the number of people in the scene, their body and head locations, and their head pose, in a joint state-space formulation that is amenable for person interaction modeling. The model exploits both global measurements and individual observations for the VFOA. For inference in the resulting high-dimensional state-space, we propose a trans-dimensional Markov Chain Monte Carlo (MCMC) sampling scheme, which not only handles a varying number of people, but also efficiently searches the state-space by allowing personpart state updates. Our model was rigorously evaluated for tracking and its ability to recognize when people look at an outdoor advertisement using a realistic data set.