Where to look at the movies : Analyzing visual attention to understand movie editing

TL;DR

This paper introduces a new eye-tracking dataset for movies, analyzing how editing influences viewer gaze patterns and exploring how computational saliency models perform on cinematic content.

Contribution

It provides a novel dataset linking movie editing with gaze data and evaluates saliency models, advancing understanding of visual attention in film viewing.

Findings

Strong links between editing and gaze patterns

Saliency models show varying performance on cinematic content

Dataset enables future research on attention modeling in movies

Abstract

In the process of making a movie, directors constantly care about where the spectator will look on the screen. Shot composition, framing, camera movements or editing are tools commonly used to direct attention. In order to provide a quantitative analysis of the relationship between those tools and gaze patterns, we propose a new eye-tracking database, containing gaze pattern information on movie sequences, as well as editing annotations, and we show how state-of-the-art computational saliency techniques behave on this dataset. In this work, we expose strong links between movie editing and spectators scanpaths, and open several leads on how the knowledge of editing information could improve human visual attention modeling for cinematic content. The dataset generated and analysed during the current study is available at https://github.com/abruckert/eye_tracking_filmmaking