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Right Inflight? A Dataset for Exploring the Automatic Prediction of Movies Suitable for a Watching Situation

Description

The dataset Right Inflight was developed to support the exploration of the match between video content and the situation in which that content is watched. Specifically, we look at videos that are suitable to be watched on an airplane, where the main assumption is that that viewers watch movies with the intent of relaxing themselves and letting time pass quickly, despite the inconvenience and discomfort of flight. The aim of the dataset is to support the development of recommender systems, as well as computer vision and multimedia retrieval algorithms capable of automatically predicting which videos are suitable for inflight consumption. Our ultimate goal is to promote a deeper understanding of how people experience video content, and of how technology can support people in finding or selecting video content that supports them in regulating their internal states in certain situations. Right Inflight consists of 318 human-annotated movies, for which we provide links to trailers, a set of pre-computed low-level visual, audio and text features as well as user ratings. The annotation was performed by crowdsourcing workers, who were asked to judge the appropriateness of movies for inflight consumption.

Access

The files are available for download via Dropbox. Link: https://www.dropbox.com/sh/j7nunecnzfjrp2r/AAC1BAf5JEv-rGUW9h02L2X2a?dl=0

Website

References and Citation

Use of the datasets in published work should be acknowledged by a full citation to the authors’ papers [RLS16] at the MMSys conference: Proceedings of ACM MMSys’16, Klagenfurt am Wörthersee, Austria, May 10-13, 2016.

References

• RLS16: Riegler, M., Larson, M., Spampinato, C., Halvorsen, P., Lux, M., Markussen, J., Pogorelov, K., Griwodz, C., Stensland, H. Right Inflight? A dataset for exploring the automatic prediction of movies suitable for a watching situation, Proceedings of the 7th International Conference on Multimedia Systems, MMSys 2016, pp. 349-355.