  In this paper we address the problems of detecting objects of interest in a
video and of estimating their locations, solely from the gaze directions of
people present in the video. Objects can be indistinctly located inside or
outside the camera field of view. We refer to this problem as extended gaze
following. The contributions of the paper are the followings. First, we propose
a novel spatial representation of the gaze directions adopting a top-view
perspective. Second, we develop several convolutional encoder/decoder networks
to predict object locations and compare them with heuristics and with classical
learning-based approaches. Third, in order to train the proposed models, we
generate a very large number of synthetic scenarios employing a probabilistic
formulation. Finally, our methodology is empirically validated using a publicly
available dataset.
