Multiple People: Wandering Visual Focus of Attention Estimation

The problem of finding the visual focus of attention of multiple people free to move in an uncons-trained manner is defined here as the wandering visual focus of attention (WVFOA) problem. Estimating the WVFOA for multiple unconstrained people is a new and important problem with implications for human behavior understanding and cognitive science, as well as real-world applications. One such application, which we present in this article, monitors the attention passers-by pay to an outdoor advertisement. In our approach to the WVFOA problem, we propose a multi-person tracking solution based on a hybrid Dynamic Bayesian Network that simultaneously infers the number of people in a scene, their body locations, their head locations, and their head pose. It is defined in a joint state-space formulation that allows for the modeling of interactions between people. 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 person-part state updates. Our model was rigorously evaluated for tracking quality and ability to recognize people looking at an outdoor advertisement, and the results indicate good performance for these tasks. 2 IDIAP–RR 06-39 FIG. 1 – In the WVFOA problem, allowing the unconstrained motion of multiple people complicates the task of estimating a subject's visual focus of attention (VFOA). Here, the resolution is too low to estimate his focus of attention from eye gaze.