Tracking Attention for 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 unconstrained manner is defined here as the {\em 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.

Related material