  This paper presents a new way of getting high-quality saliency maps for
video, using a cheaper alternative to eye-tracking data. We designed a
mouse-contingent video viewing system which simulates the viewers' peripheral
vision based on the position of the mouse cursor. The system enables the use of
mouse-tracking data recorded from an ordinary computer mouse as an alternative
to real gaze fixations recorded by a more expensive eye-tracker. We developed a
crowdsourcing system that enables the collection of such mouse-tracking data at
large scale. Using the collected mouse-tracking data we showed that it can
serve as an approximation of eye-tracking data. Moreover, trying to increase
the efficiency of collected mouse-tracking data we proposed a novel deep neural
network algorithm that improves the quality of mouse-tracking saliency maps.
