Detecting repeated motion patterns via dynamic programming using motion density

Authors:
Koichi Ogawara;Yasufumi Tanabe;Ryo Kurazume;Tsutomu Hasegawa
Affiliations:
Faculty of Engineering, Kyushu University, Fukuoka, Japan;Department of Electrical Engineering and Computer Science, Kyushu University, Fukuoka, Japan;Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan;Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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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.