ICRA'09 Paper Abstract

Close

Paper FrA6.5

Ogawara, Koichi (Kyushu University), Tanabe, Yasufumi (Kyushu University), Kurazume, Ryo (Kyushu University), Hasegawa, Tsutomu (Kyushu University)

Detecting Repeated Motion Patterns via Dynamic Programming using Motion Density

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems - I" (FrA6), Friday, May 15, 2009, 09:50−10:10, Room: 404

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 21, 2022

Keywords AI Reasoning Methods, Recognition, Service Robots

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 log N) 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.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2022 PaperCept, Inc.
Page generated 2022-01-21  10:11:23 PST  Terms of use