Gaze Location Prediction for Broadcast Football Video

Qin Cheng, Dimitris Agrafiotis, Alin Achim, David Bull

Research output: Contribution to journalArticle (Academic Journal)peer-review

8 Citations (Scopus)

Abstract

The sensitivity of the human visual system decreases dramatically with increasing distance from the fixation location in a video frame. Accurate prediction of a viewer's gaze location has the potential to improve bit allocation, rate control, error resilience and quality evaluation in video compression. Commercially, delivery of football video content is of great interest due to the very high number of consumers. In this paper we propose a gaze location prediction system for high definition broadcast football video. The proposed system uses knowledge about the context, extracted through analysis of a gaze tracking study that we performed, in order to build a suitable prior map. We further classify the complex context into different categories through shot classification thus allowing our model to pre-learn the task pertinence of each object category and build the prior map automatically. We thus avoid the limitation of assigning the viewers a specific task, allowing our gaze prediction system to work under free-viewing conditions. Bayesian integration of bottom-up features and top-down priors is finally applied to predict the gaze locations. Results show that the prediction performance of the proposed model is better than that of other top-down models which we adapted to this context.
Original languageEnglish
JournalIEEE Transactions on Image Processing
Early online date28 Aug 2013
DOIs
Publication statusPublished - Dec 2013

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