Novelty-based Spatiotemporal Saliency Detection for Prediction of Gaze in Egocentric Video
Publikation aus Digital
Polatsek, P. and Benesova, W. and Paletta, DI Dr. Lucas Paletta, Perko, R.
Signal Processing Letters, IEEE , 1/2016
The automated analysis of video captured from a first-person perspective has gained increased interest since the advent of marketed miniaturised wearable cameras. With this a person is taking visual measurements about the world in a sequence of fixations which contain relevant information about the most salient parts of the environment and the goals of the actor. We present a novel model for gaze prediction in egocentric video based on the spatiotemporal visual information captured from the wearer's camera, specifically extended using a subjective function of surprise by means of motion memory, referring to the human aspect of visual attention. Spatiotemporal saliency detection is computed in a bioinspired framework using a superposition of superpixel- and contrast based conspicuity maps as well as an optical flow based motion saliency map. Motion is further processed into a motion novelty map that is constructed by a comparison between most recent motion information with an exponentially decreasing memory of motion information. The innovative motion novelty map is experienced to be able to provide a significant increase in the performance of gaze prediction. Experimental results are gained from egocentric
videos using eye-tracking glasses in a natural shopping task and prove a 6.48% increase in the mean saliency at a fixation in terms of a measure of mimicking human attention.