Dynamic predictions of tracked gaze

We present a model for predicting the eye-movements of observers who is viewing dynamic sequences of images. As an indicator for the degree of saliency we evaluate an invariant of the spatio-temporal structure tensor that indicates an intrinsic dimension of at least two. The saliency is used to derive a list of candidate locations. Out of this list, the currently attended location is selected according to a mapping found by supervised learning. The true locations used for learning are obtained with an eyetracker. In addition to the saliency-based candidates, the selection algorithm uses a limited history of locations attended in the past. The mapping is linear and can thus be quickly adapted to the individual observer. The mapping is optimal in the sense that it is obtained by minimizing, by gradient descent, the overall quadratic difference between the predicted and the actually attended location.