Predicting movie ratings from audience behaviors

Navarathna, Rajitha, Lucey, Patrick J., Carr, Peter, Carter, Elizabeth Anne, Sridharan, Sridha, & Matthews, Iain (2014) Predicting movie ratings from audience behaviors. In Proceedings of the 2014 IEEE Winter Conference on Applications of Computer Vision (WACV 2014), IEEE, Steamboat Springs, CO, pp. 1058-1065.

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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 and show our prediction capability on audiences from 30 movies across 250 subjects (> 50 hrs).

Impact and interest:

3 citations in Scopus
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1 citations in Web of Science®

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ID Code: 74881
Item Type: Conference Paper
Refereed: Yes
Keywords: Forecasting, Video streaming, Target tracking, Body motions, Movie ratings, Group representation, Prediction capability, Unsolved problems
DOI: 10.1109/WACV.2014.6835987
ISBN: 9781479949854
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Deposited On: 12 Aug 2014 03:14
Last Modified: 15 Aug 2014 17:09

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