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Predicting video saliency using crowdsourced mouse-tracking dataстатья
- Авторы: Vitaliy Lyudvichenko, Dmitriy Vatolin
- Сборник: Proceedings of the 29th International Conference on Computer Graphics and Vision
- Том: 2485
- Год издания: 2019
- Место издания: CEUR Workshop Proceedings
- Первая страница: 127
- Последняя страница: 130
- DOI: 10.30987/graphicon-2019-2-127-130
- Аннотация: 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.
- Добавил в систему: Людвиченко Виталий Андреевич
Прикрепленные файлы
| № | Имя | Описание | Имя файла | Размер | Добавлен |
|---|---|---|---|---|---|
| 1. | Полный текст | paper29.pdf | 1,5 МБ | 23 апреля 2020 [dmitriyv] | |
| 2. | Predicting_video_saliency_using_crowdsourced_mouse_tracking… | Predicting_video_saliency_using_crowdsourced_mouse_tracking… | 351,5 КБ | 23 ноября 2019 [vlyudvichenko] |