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Authors: Dan Mikami ; Kazuhiro Otsuka ; Shiro Kumano and Junji Yamato

Affiliation: NTT, Japan

ISBN: 978-989-8565-04-4

Keyword(s): Pose Tracking, Face Pose, Memory-based Prediction, Memory Acquisition.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Enterprise Information Systems ; Human and Computer Interaction ; Human-Computer Interaction ; Motion, Tracking and Stereo Vision ; Tracking and Visual Navigation

Abstract: A novel enhancement for the memory-based particle filter is proposed for visual pose tracking under severe occlusions. The enhancement is the addition of a detection-based memory acquisition mechanism. The memorybased particle filter, M-PF, is a particle filter that predicts prior distributions from past history of target state, which achieved high robustness against complex dynamics of a tracking target. Such high performance requires sufficient history stored in memory. Conventionally, M-PF conducts online memory acquisition which assumes simple target’s dynamics without occlusions for guaranteeing high quality histories. The requirement of memory acquisition narrows the coverage of M-PF in practice. In this paper, we propose a new memory acquisition mechanism for M-PF. The key idea is to use a target detector that can produce additional prior distribution of the target state. We call it M-PFDMA for M-PF with detection-based memory acquisition. The detection-based prior distribution well predicts possible target position/pose even in limited visibility conditions caused by occlusions. Such better prior distributions contribute to stable estimation of target state, which is then added to memorized data. As a result, M-PFDMA can start with no memory entries but soon achieve stable tracking even under severe occlusions. Experiments confirm M-PFDMA’s good performance in such conditions. (More)

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Paper citation in several formats:
Mikami, D.; Otsuka, K.; Kumano, S. and Yamato, J. (2012). ENHANCING MEMORY-BASED PARTICLE FILTER WITH DETECTION-BASED MEMORY ACQUISITION FOR ROBUSTNESS UNDER SEVERE OCCLUSION.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-04-4, pages 208-215. DOI: 10.5220/0003808302080215

@conference{visapp12,
author={Dan Mikami. and Kazuhiro Otsuka. and Shiro Kumano. and Junji Yamato.},
title={ENHANCING MEMORY-BASED PARTICLE FILTER WITH DETECTION-BASED MEMORY ACQUISITION FOR ROBUSTNESS UNDER SEVERE OCCLUSION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={208-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003808302080215},
isbn={978-989-8565-04-4},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - ENHANCING MEMORY-BASED PARTICLE FILTER WITH DETECTION-BASED MEMORY ACQUISITION FOR ROBUSTNESS UNDER SEVERE OCCLUSION
SN - 978-989-8565-04-4
AU - Mikami, D.
AU - Otsuka, K.
AU - Kumano, S.
AU - Yamato, J.
PY - 2012
SP - 208
EP - 215
DO - 10.5220/0003808302080215

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