Contribution estimation of participants for human interaction recognition

Yanli Ji, Atsushi Shimada, Hajime Nagahara, Rin-Ichiro Taniguchi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this paper, we propose an efficient algorithm to recognize actions of human interaction. Unlike previous algorithms using two participants' actions, the proposed algorithm estimates the action contribution of participants to determine which participant's action is the major action for correct interaction recognition. To estimate this contribution, we construct a contribution interaction model for each interaction category in which both participants carry out major actions. Using the contribution models, we design a method that automatically estimates the contribution of participants and classifies interaction samples into "co-contribution" and "single-contribution" interactions. At the same time, the major actions in the "single-contribution" interactions are determined. We evaluate our method on the UT-interaction dataset and our original interaction dataset (LIMU). Recognition results indicate the robustness of the proposed method and the high estimation accuracy obtained: estimation accuracies of 96 and 98% in set 1 and set 2 of the UT dataset, respectively, and 97.8% in the LIMU dataset. Based on the estimation results, we extract the major action information for interaction recognition. Average recognition accuracies of 93.3% in set 1 and 91.7% in set 2 of the UT dataset were obtained. Our result is at least 5% better than those obtained with previous algorithms. For the LIMU dataset, recognition accuracy reached 91.1%. It was 8.9% higher than the recognition result without contribution estimation.

Original languageEnglish
Pages (from-to)269-276
Number of pages8
JournalIEEJ Transactions on Electrical and Electronic Engineering
Volume8
Issue number3
DOIs
Publication statusPublished - Jan 1 2013

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Contribution estimation of participants for human interaction recognition. / Ji, Yanli; Shimada, Atsushi; Nagahara, Hajime; Taniguchi, Rin-Ichiro.

In: IEEJ Transactions on Electrical and Electronic Engineering, Vol. 8, No. 3, 01.01.2013, p. 269-276.

Research output: Contribution to journalArticle

@article{30c5905754dc4944bca4ae7b47c9adec,
title = "Contribution estimation of participants for human interaction recognition",
abstract = "In this paper, we propose an efficient algorithm to recognize actions of human interaction. Unlike previous algorithms using two participants' actions, the proposed algorithm estimates the action contribution of participants to determine which participant's action is the major action for correct interaction recognition. To estimate this contribution, we construct a contribution interaction model for each interaction category in which both participants carry out major actions. Using the contribution models, we design a method that automatically estimates the contribution of participants and classifies interaction samples into {"}co-contribution{"} and {"}single-contribution{"} interactions. At the same time, the major actions in the {"}single-contribution{"} interactions are determined. We evaluate our method on the UT-interaction dataset and our original interaction dataset (LIMU). Recognition results indicate the robustness of the proposed method and the high estimation accuracy obtained: estimation accuracies of 96 and 98{\%} in set 1 and set 2 of the UT dataset, respectively, and 97.8{\%} in the LIMU dataset. Based on the estimation results, we extract the major action information for interaction recognition. Average recognition accuracies of 93.3{\%} in set 1 and 91.7{\%} in set 2 of the UT dataset were obtained. Our result is at least 5{\%} better than those obtained with previous algorithms. For the LIMU dataset, recognition accuracy reached 91.1{\%}. It was 8.9{\%} higher than the recognition result without contribution estimation.",
author = "Yanli Ji and Atsushi Shimada and Hajime Nagahara and Rin-Ichiro Taniguchi",
year = "2013",
month = "1",
day = "1",
doi = "10.1002/tee.21850",
language = "English",
volume = "8",
pages = "269--276",
journal = "IEEJ Transactions on Electrical and Electronic Engineering",
issn = "1931-4973",
publisher = "John Wiley and Sons Inc.",
number = "3",

}

TY - JOUR

T1 - Contribution estimation of participants for human interaction recognition

AU - Ji, Yanli

AU - Shimada, Atsushi

AU - Nagahara, Hajime

AU - Taniguchi, Rin-Ichiro

PY - 2013/1/1

Y1 - 2013/1/1

N2 - In this paper, we propose an efficient algorithm to recognize actions of human interaction. Unlike previous algorithms using two participants' actions, the proposed algorithm estimates the action contribution of participants to determine which participant's action is the major action for correct interaction recognition. To estimate this contribution, we construct a contribution interaction model for each interaction category in which both participants carry out major actions. Using the contribution models, we design a method that automatically estimates the contribution of participants and classifies interaction samples into "co-contribution" and "single-contribution" interactions. At the same time, the major actions in the "single-contribution" interactions are determined. We evaluate our method on the UT-interaction dataset and our original interaction dataset (LIMU). Recognition results indicate the robustness of the proposed method and the high estimation accuracy obtained: estimation accuracies of 96 and 98% in set 1 and set 2 of the UT dataset, respectively, and 97.8% in the LIMU dataset. Based on the estimation results, we extract the major action information for interaction recognition. Average recognition accuracies of 93.3% in set 1 and 91.7% in set 2 of the UT dataset were obtained. Our result is at least 5% better than those obtained with previous algorithms. For the LIMU dataset, recognition accuracy reached 91.1%. It was 8.9% higher than the recognition result without contribution estimation.

AB - In this paper, we propose an efficient algorithm to recognize actions of human interaction. Unlike previous algorithms using two participants' actions, the proposed algorithm estimates the action contribution of participants to determine which participant's action is the major action for correct interaction recognition. To estimate this contribution, we construct a contribution interaction model for each interaction category in which both participants carry out major actions. Using the contribution models, we design a method that automatically estimates the contribution of participants and classifies interaction samples into "co-contribution" and "single-contribution" interactions. At the same time, the major actions in the "single-contribution" interactions are determined. We evaluate our method on the UT-interaction dataset and our original interaction dataset (LIMU). Recognition results indicate the robustness of the proposed method and the high estimation accuracy obtained: estimation accuracies of 96 and 98% in set 1 and set 2 of the UT dataset, respectively, and 97.8% in the LIMU dataset. Based on the estimation results, we extract the major action information for interaction recognition. Average recognition accuracies of 93.3% in set 1 and 91.7% in set 2 of the UT dataset were obtained. Our result is at least 5% better than those obtained with previous algorithms. For the LIMU dataset, recognition accuracy reached 91.1%. It was 8.9% higher than the recognition result without contribution estimation.

UR - http://www.scopus.com/inward/record.url?scp=84876500166&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876500166&partnerID=8YFLogxK

U2 - 10.1002/tee.21850

DO - 10.1002/tee.21850

M3 - Article

AN - SCOPUS:84876500166

VL - 8

SP - 269

EP - 276

JO - IEEJ Transactions on Electrical and Electronic Engineering

JF - IEEJ Transactions on Electrical and Electronic Engineering

SN - 1931-4973

IS - 3

ER -