University of Bristol

Visual saliency

By being able to predict multicue gaze for open signed video content, there can be coding gains without loss of perceived quality. vissWe have developed a face orientation tracker based upon grid-based likelihood ratio trackers, using profile and frontal face detections. These cues are combined using a grid-based Bayesian state estimation algorithm to form a probability surface for each frame. This gaze predictor outperforms a static gaze prediction and one based on face locations within the frame.

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