Detecting repeated motion patterns via dynamic programming using motion density

Koichi Ogawara, Yasufumi Tanabe, Ryo Kurazume, Tsutomu Hasegawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, we propose a method that detects repeated motion patterns in a long motion sequence efficiently. Repeated motion patterns are the structured information that can be obtained without knowledge of the context of motions. They can be used as a seed to find causal relationships between motions or to obtain contextual information of human activity, which is useful for intelligent systems that support human activity in everyday environment. The major contribution of the proposed method is two-fold: (1) motion density is proposed as a repeatability measure and (2) the problem of finding consecutive time frames with large motion density is formulated as a combinatorial optimization problem which is solved via Dynamic Programming (DP) in polynomial time O(N logN) where N is the total amount of data. The proposed method was evaluated by detecting repeated interactions between objects in everyday manipulation tasks and outperformed the previous method in terms of both detectability and computational time.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Robotics and Automation, ICRA '09
Pages1743-1749
Number of pages7
DOIs
Publication statusPublished - Nov 2 2009
Event2009 IEEE International Conference on Robotics and Automation, ICRA '09 - Kobe, Japan
Duration: May 12 2009May 17 2009

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2009 IEEE International Conference on Robotics and Automation, ICRA '09
CountryJapan
CityKobe
Period5/12/095/17/09

Fingerprint

Combinatorial optimization
Intelligent systems
Dynamic programming
Seed
Polynomials

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Ogawara, K., Tanabe, Y., Kurazume, R., & Hasegawa, T. (2009). Detecting repeated motion patterns via dynamic programming using motion density. In 2009 IEEE International Conference on Robotics and Automation, ICRA '09 (pp. 1743-1749). [5152643] (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ROBOT.2009.5152643

Detecting repeated motion patterns via dynamic programming using motion density. / Ogawara, Koichi; Tanabe, Yasufumi; Kurazume, Ryo; Hasegawa, Tsutomu.

2009 IEEE International Conference on Robotics and Automation, ICRA '09. 2009. p. 1743-1749 5152643 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ogawara, K, Tanabe, Y, Kurazume, R & Hasegawa, T 2009, Detecting repeated motion patterns via dynamic programming using motion density. in 2009 IEEE International Conference on Robotics and Automation, ICRA '09., 5152643, Proceedings - IEEE International Conference on Robotics and Automation, pp. 1743-1749, 2009 IEEE International Conference on Robotics and Automation, ICRA '09, Kobe, Japan, 5/12/09. https://doi.org/10.1109/ROBOT.2009.5152643
Ogawara K, Tanabe Y, Kurazume R, Hasegawa T. Detecting repeated motion patterns via dynamic programming using motion density. In 2009 IEEE International Conference on Robotics and Automation, ICRA '09. 2009. p. 1743-1749. 5152643. (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ROBOT.2009.5152643
Ogawara, Koichi ; Tanabe, Yasufumi ; Kurazume, Ryo ; Hasegawa, Tsutomu. / Detecting repeated motion patterns via dynamic programming using motion density. 2009 IEEE International Conference on Robotics and Automation, ICRA '09. 2009. pp. 1743-1749 (Proceedings - IEEE International Conference on Robotics and Automation).
@inproceedings{977c943759f3409f823b84465766f3f7,
title = "Detecting repeated motion patterns via dynamic programming using motion density",
abstract = "In this paper, we propose a method that detects repeated motion patterns in a long motion sequence efficiently. Repeated motion patterns are the structured information that can be obtained without knowledge of the context of motions. They can be used as a seed to find causal relationships between motions or to obtain contextual information of human activity, which is useful for intelligent systems that support human activity in everyday environment. The major contribution of the proposed method is two-fold: (1) motion density is proposed as a repeatability measure and (2) the problem of finding consecutive time frames with large motion density is formulated as a combinatorial optimization problem which is solved via Dynamic Programming (DP) in polynomial time O(N logN) where N is the total amount of data. The proposed method was evaluated by detecting repeated interactions between objects in everyday manipulation tasks and outperformed the previous method in terms of both detectability and computational time.",
author = "Koichi Ogawara and Yasufumi Tanabe and Ryo Kurazume and Tsutomu Hasegawa",
year = "2009",
month = "11",
day = "2",
doi = "10.1109/ROBOT.2009.5152643",
language = "English",
isbn = "9781424427895",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
pages = "1743--1749",
booktitle = "2009 IEEE International Conference on Robotics and Automation, ICRA '09",

}

TY - GEN

T1 - Detecting repeated motion patterns via dynamic programming using motion density

AU - Ogawara, Koichi

AU - Tanabe, Yasufumi

AU - Kurazume, Ryo

AU - Hasegawa, Tsutomu

PY - 2009/11/2

Y1 - 2009/11/2

N2 - In this paper, we propose a method that detects repeated motion patterns in a long motion sequence efficiently. Repeated motion patterns are the structured information that can be obtained without knowledge of the context of motions. They can be used as a seed to find causal relationships between motions or to obtain contextual information of human activity, which is useful for intelligent systems that support human activity in everyday environment. The major contribution of the proposed method is two-fold: (1) motion density is proposed as a repeatability measure and (2) the problem of finding consecutive time frames with large motion density is formulated as a combinatorial optimization problem which is solved via Dynamic Programming (DP) in polynomial time O(N logN) where N is the total amount of data. The proposed method was evaluated by detecting repeated interactions between objects in everyday manipulation tasks and outperformed the previous method in terms of both detectability and computational time.

AB - In this paper, we propose a method that detects repeated motion patterns in a long motion sequence efficiently. Repeated motion patterns are the structured information that can be obtained without knowledge of the context of motions. They can be used as a seed to find causal relationships between motions or to obtain contextual information of human activity, which is useful for intelligent systems that support human activity in everyday environment. The major contribution of the proposed method is two-fold: (1) motion density is proposed as a repeatability measure and (2) the problem of finding consecutive time frames with large motion density is formulated as a combinatorial optimization problem which is solved via Dynamic Programming (DP) in polynomial time O(N logN) where N is the total amount of data. The proposed method was evaluated by detecting repeated interactions between objects in everyday manipulation tasks and outperformed the previous method in terms of both detectability and computational time.

UR - http://www.scopus.com/inward/record.url?scp=70350356827&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70350356827&partnerID=8YFLogxK

U2 - 10.1109/ROBOT.2009.5152643

DO - 10.1109/ROBOT.2009.5152643

M3 - Conference contribution

AN - SCOPUS:70350356827

SN - 9781424427895

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 1743

EP - 1749

BT - 2009 IEEE International Conference on Robotics and Automation, ICRA '09

ER -