Predicting video saliency using crowdsourced mouse-tracking data
This provides a more accessible method for video saliency prediction, particularly for researchers and applications lacking expensive eye-tracking equipment, though it is incremental in improving data collection efficiency.
The paper tackled the problem of generating high-quality video saliency maps by introducing a cheaper alternative to eye-tracking using mouse-tracking data, showing that it approximates eye-tracking data and proposing a deep neural network algorithm to improve saliency map quality.
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.