Predicting Movie Ratings from Audience Behaviors
Abstract
We propose a method of representing audience behavior through facial and body motions from a single video stream, and use these features to predict the rating for feature-length movies. This is a very challenging problem as: i) the movie viewing environment is dark and contains views of people at different scales and viewpoints; ii) the duration of feature-length movies is long (80-120 mins) so tracking people uninterrupted for this length of time is still an unsolved problem, and; iii) expressions and motions of audience members are subtle, short and sparse making labeling of activities unreliable. To circumvent these issues, we use an infrared illuminated test-bed to obtain a visually uniform input. We then utilize motion-history features which capture the subtle movements of a person within a pre-defined volume, and then form a group representation of the audience by a histogram of pair-wise correlations over a small-window of time. Using this group representation, we learn our movie rating classifier from crowd-sourced ratings collected by rottentomatoes.com and show our prediction capability on audiences from 30 movies across 250 subjects (> 50 hrs).
Cite
Text
Navarathna et al. "Predicting Movie Ratings from Audience Behaviors." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6835987Markdown
[Navarathna et al. "Predicting Movie Ratings from Audience Behaviors." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/navarathna2014wacv-predicting/) doi:10.1109/WACV.2014.6835987BibTeX
@inproceedings{navarathna2014wacv-predicting,
title = {{Predicting Movie Ratings from Audience Behaviors}},
author = {Navarathna, Rajitha and Lucey, Patrick and Carr, Peter and Carter, Elizabeth J. and Sridharan, Sridha and Matthews, Iain A.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2014},
pages = {1058-1065},
doi = {10.1109/WACV.2014.6835987},
url = {https://mlanthology.org/wacv/2014/navarathna2014wacv-predicting/}
}