LEARNING A VISUAL ATTENTION MODEL FOR ADAPTIVE FAST-FORWARD IN VIDEO SURVEILLANCE

Benjamin Höferlin, Hermann Pflüger, Markus Höferlin, Gunther Heidemann, Daniel Weiskopf

Abstract

The focus of visual attention is guided by salient signals in the peripheral field of view (bottom-up) as well as by the relevance feedback of a semantic model (top-down). As a result, humans are able to evaluate new situations very fast, with only a view numbers of fixations. In this paper, we present a learned model for the fast prediction of visual attention in video. We consider bottom-up and memory-less top-down mechanisms of visual attention guidance, and apply the model to video playback-speed adaption. The presented visual attention model is based on rectangle features that are fast to compute and capable of describing the known mechanisms of bottom-up processing, such as motion, contrast, color, symmetry, and others as well as topdown cues, such as face and person detectors. We show that the visual attention model outperforms other recent methods in adaption of video playback-speed.

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Paper Citation


in Harvard Style

Höferlin B., Pflüger H., Höferlin M., Heidemann G. and Weiskopf D. (2012). LEARNING A VISUAL ATTENTION MODEL FOR ADAPTIVE FAST-FORWARD IN VIDEO SURVEILLANCE . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-8425-99-7, pages 25-32. DOI: 10.5220/0003720000250032


in Bibtex Style

@conference{icpram12,
author={Benjamin Höferlin and Hermann Pflüger and Markus Höferlin and Gunther Heidemann and Daniel Weiskopf},
title={LEARNING A VISUAL ATTENTION MODEL FOR ADAPTIVE FAST-FORWARD IN VIDEO SURVEILLANCE},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2012},
pages={25-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003720000250032},
isbn={978-989-8425-99-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - LEARNING A VISUAL ATTENTION MODEL FOR ADAPTIVE FAST-FORWARD IN VIDEO SURVEILLANCE
SN - 978-989-8425-99-7
AU - Höferlin B.
AU - Pflüger H.
AU - Höferlin M.
AU - Heidemann G.
AU - Weiskopf D.
PY - 2012
SP - 25
EP - 32
DO - 10.5220/0003720000250032