Probabilistic cluster signature for modeling motion classes

Shandong Wu; Y. F. Li; Jianwei Zhang

doi:10.1109/IROS.2009.5354142

Probabilistic cluster signature for modeling motion classes

Shandong Wu, Y. F. Li, Jianwei Zhang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

In this paper, a novel 3-D motion trajectory signature is introduced to serve as an effective description to the raw trajectory. More importantly, based on the trajectory signature, a probabilistic model-based cluster signature is further developed for modeling a motion class. The cluster signature is a mixture model-based motion description that is useful for motion class perception, recognition and to benefit a generalized robot task representation. The signature modeling process is supported by integrating the EM and IPRA algorithms. The conducted experiments verified the cluster signature's effectiveness. © 2009 IEEE.

Original language	English
Title of host publication	2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Pages	5731-5736
DOIs	https://doi.org/10.1109/IROS.2009.5354142
Publication status	Published - 11 Dec 2009
Event	2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009) - St. Louis, United States Duration: 11 Oct 2009 → 15 Oct 2009

Conference

Conference	2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009)
Place	United States
City	St. Louis
Period	11/10/09 → 15/10/09

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title = "Probabilistic cluster signature for modeling motion classes",

abstract = "In this paper, a novel 3-D motion trajectory signature is introduced to serve as an effective description to the raw trajectory. More importantly, based on the trajectory signature, a probabilistic model-based cluster signature is further developed for modeling a motion class. The cluster signature is a mixture model-based motion description that is useful for motion class perception, recognition and to benefit a generalized robot task representation. The signature modeling process is supported by integrating the EM and IPRA algorithms. The conducted experiments verified the cluster signature's effectiveness. {\textcopyright} 2009 IEEE.",

author = "Shandong Wu and Li, {Y. F.} and Jianwei Zhang",

year = "2009",

month = dec,

day = "11",

doi = "10.1109/IROS.2009.5354142",

language = "English",

isbn = "9781424438044",

pages = "5731--5736",

booktitle = "2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009",

note = "2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009) ; Conference date: 11-10-2009 Through 15-10-2009",

}

Wu, S, Li, YF & Zhang, J 2009, Probabilistic cluster signature for modeling motion classes. in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009., 5354142, pp. 5731-5736, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), St. Louis, Missouri, United States, 11/10/09. https://doi.org/10.1109/IROS.2009.5354142

TY - GEN

T1 - Probabilistic cluster signature for modeling motion classes

AU - Wu, Shandong

AU - Li, Y. F.

AU - Zhang, Jianwei

PY - 2009/12/11

Y1 - 2009/12/11

N2 - In this paper, a novel 3-D motion trajectory signature is introduced to serve as an effective description to the raw trajectory. More importantly, based on the trajectory signature, a probabilistic model-based cluster signature is further developed for modeling a motion class. The cluster signature is a mixture model-based motion description that is useful for motion class perception, recognition and to benefit a generalized robot task representation. The signature modeling process is supported by integrating the EM and IPRA algorithms. The conducted experiments verified the cluster signature's effectiveness. © 2009 IEEE.

AB - In this paper, a novel 3-D motion trajectory signature is introduced to serve as an effective description to the raw trajectory. More importantly, based on the trajectory signature, a probabilistic model-based cluster signature is further developed for modeling a motion class. The cluster signature is a mixture model-based motion description that is useful for motion class perception, recognition and to benefit a generalized robot task representation. The signature modeling process is supported by integrating the EM and IPRA algorithms. The conducted experiments verified the cluster signature's effectiveness. © 2009 IEEE.

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-76249101837&origin=recordpage

U2 - 10.1109/IROS.2009.5354142

DO - 10.1109/IROS.2009.5354142

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781424438044

SP - 5731

EP - 5736

BT - 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009

T2 - 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009)

Y2 - 11 October 2009 through 15 October 2009

ER -

Probabilistic cluster signature for modeling motion classes

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