loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Benjamin Höferlin 1 ; Hermann Pflüger 1 ; Markus Höferlin 1 ; Gunther Heidemann 2 and Daniel Weiskopf 1

Affiliations: 1 University of Stuttgart, Germany ; 2 University of Osnabrück, Germany

ISBN: 978-989-8425-99-7

Keyword(s): Visual attention, Adaptive fast-forward, Video surveillance.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Pattern Recognition ; Perception ; Software Engineering ; Video Analysis

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.

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.92.174.226

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.