Discriminative random fields for behavior modeling

Huang Tianyu; Shi Chongde; Li Fengxia

doi:10.1109/CSIE.2009.594

Discriminative random fields for behavior modeling

Huang Tianyu, Shi Chongde, Li Fengxia

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

This paper proposed an approach of human behavior modeling based on Discriminative Random Fields. In this model, by introducing the hidden behavior feature functions and time window parameters, the Classical CRFs models was extended to spatio-temporal fields. And feature templates were designed to capture the dynamics of human motions. Due to the conditional structure, this model can accommodate arbitrary overlapping features of the observation as well as long-term contextual dependencies among observations. Behavior recognition method was designed in the experiments. And the results proved that the proposed modeling method performed over than HMM and CRF for human behavior modeling.

Original language	English
Title of host publication	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Pages	17-21
Number of pages	5
DOIs	https://doi.org/10.1109/CSIE.2009.594
Publication status	Published - 2009
Event	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 - Los Angeles, CA, United States Duration: 31 Mar 2009 → 2 Apr 2009

Publication series

Name	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Volume	5

Conference

Conference	2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Country/Territory	United States
City	Los Angeles, CA
Period	31/03/09 → 2/04/09

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10.1109/CSIE.2009.594

Cite this

@inproceedings{12fecd02d3cb4f8bb570b4f408a3a648,

title = "Discriminative random fields for behavior modeling",

abstract = "This paper proposed an approach of human behavior modeling based on Discriminative Random Fields. In this model, by introducing the hidden behavior feature functions and time window parameters, the Classical CRFs models was extended to spatio-temporal fields. And feature templates were designed to capture the dynamics of human motions. Due to the conditional structure, this model can accommodate arbitrary overlapping features of the observation as well as long-term contextual dependencies among observations. Behavior recognition method was designed in the experiments. And the results proved that the proposed modeling method performed over than HMM and CRF for human behavior modeling.",

author = "Huang Tianyu and Shi Chongde and Li Fengxia",

year = "2009",

doi = "10.1109/CSIE.2009.594",

language = "English",

isbn = "9780769535074",

series = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009",

pages = "17--21",

booktitle = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009",

note = "2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 ; Conference date: 31-03-2009 Through 02-04-2009",

}

Tianyu, H, Chongde, S & Fengxia, L 2009, Discriminative random fields for behavior modeling. in 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009., 5170488, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, vol. 5, pp. 17-21, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, Los Angeles, CA, United States, 31/03/09. https://doi.org/10.1109/CSIE.2009.594

Discriminative random fields for behavior modeling. / Tianyu, Huang; Chongde, Shi; Fengxia, Li.
2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009. 2009. p. 17-21 5170488 (2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009; Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Chongde, Shi

AU - Fengxia, Li

PY - 2009

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N2 - This paper proposed an approach of human behavior modeling based on Discriminative Random Fields. In this model, by introducing the hidden behavior feature functions and time window parameters, the Classical CRFs models was extended to spatio-temporal fields. And feature templates were designed to capture the dynamics of human motions. Due to the conditional structure, this model can accommodate arbitrary overlapping features of the observation as well as long-term contextual dependencies among observations. Behavior recognition method was designed in the experiments. And the results proved that the proposed modeling method performed over than HMM and CRF for human behavior modeling.

AB - This paper proposed an approach of human behavior modeling based on Discriminative Random Fields. In this model, by introducing the hidden behavior feature functions and time window parameters, the Classical CRFs models was extended to spatio-temporal fields. And feature templates were designed to capture the dynamics of human motions. Due to the conditional structure, this model can accommodate arbitrary overlapping features of the observation as well as long-term contextual dependencies among observations. Behavior recognition method was designed in the experiments. And the results proved that the proposed modeling method performed over than HMM and CRF for human behavior modeling.

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T3 - 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009

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T2 - 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009

Y2 - 31 March 2009 through 2 April 2009

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Discriminative random fields for behavior modeling

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