A bio-inspired event-based size and position invariant human posture recognition algorithm

This paper proposes a new approach to recognize human postures in realtime video sequences. The algorithm employs temporal difference imaging between video sequences...

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Main Authors: Chen, Shoushun, Martini, Berin, Culurciello, Eugenio
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2010
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Online Access:https://hdl.handle.net/10356/79903
http://hdl.handle.net/10220/6363
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-799032020-03-07T13:24:43Z A bio-inspired event-based size and position invariant human posture recognition algorithm Chen, Shoushun Martini, Berin Culurciello, Eugenio School of Electrical and Electronic Engineering IEEE International Symposium on Circuits and Systems (2009 : Taipei, Taiwan) DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing This paper proposes a new approach to recognize human postures in realtime video sequences. The algorithm employs temporal difference imaging between video sequences as input and then decompose the contour of the active object into vectorial line segments. A scheme based on simplified Line Segment Hausdorff Distance combined with projection histograms is proposed to achieve size and position invariance recognition. Consistent with the hierarchical model of the human visual system, sub-sampling techniques are used to represent the object by line segments at multiple resolution levels. The whole classification is described as a coarse to fine procedure. An average realtime recognition rate of 88% is achieved in the experiment. Compared to conventional convolution method, the proposed algorithm reduces the computation cycles by 10 − 100 times. This work sets the foundation for size and position invariant object recognition for the implementation of eventbased vision systems Published version 2010-08-30T05:54:52Z 2019-12-06T13:36:26Z 2010-08-30T05:54:52Z 2019-12-06T13:36:26Z 2009 2009 Conference Paper Chen, S. S., Martini, B., & Culurciello, E. (2009). A bio-inspired event-based size and position invariant human posture recognition algorithm. In proceedings of the IEEE International Symposium on Circuits and Systems: Taipei, Taiwan, (pp.775-778). https://hdl.handle.net/10356/79903 http://hdl.handle.net/10220/6363 10.1109/ISCAS.2009.5117864 en © 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. 4 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Chen, Shoushun
Martini, Berin
Culurciello, Eugenio
A bio-inspired event-based size and position invariant human posture recognition algorithm
description This paper proposes a new approach to recognize human postures in realtime video sequences. The algorithm employs temporal difference imaging between video sequences as input and then decompose the contour of the active object into vectorial line segments. A scheme based on simplified Line Segment Hausdorff Distance combined with projection histograms is proposed to achieve size and position invariance recognition. Consistent with the hierarchical model of the human visual system, sub-sampling techniques are used to represent the object by line segments at multiple resolution levels. The whole classification is described as a coarse to fine procedure. An average realtime recognition rate of 88% is achieved in the experiment. Compared to conventional convolution method, the proposed algorithm reduces the computation cycles by 10 − 100 times. This work sets the foundation for size and position invariant object recognition for the implementation of eventbased vision systems
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Chen, Shoushun
Martini, Berin
Culurciello, Eugenio
format Conference or Workshop Item
author Chen, Shoushun
Martini, Berin
Culurciello, Eugenio
author_sort Chen, Shoushun
title A bio-inspired event-based size and position invariant human posture recognition algorithm
title_short A bio-inspired event-based size and position invariant human posture recognition algorithm
title_full A bio-inspired event-based size and position invariant human posture recognition algorithm
title_fullStr A bio-inspired event-based size and position invariant human posture recognition algorithm
title_full_unstemmed A bio-inspired event-based size and position invariant human posture recognition algorithm
title_sort bio-inspired event-based size and position invariant human posture recognition algorithm
publishDate 2010
url https://hdl.handle.net/10356/79903
http://hdl.handle.net/10220/6363
_version_ 1681035504145924096