Probabilistic cluster signature for modeling motion classes

Authors:
Shandong Wu;Y. F. Li;Jianwei Zhang
Affiliations:
Computer Vision Lab of School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong;Department of Informatics, University of Hamburg, Hamburg, Germany
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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Abstract

In this paper, a novel 3-D motion trajectory signature is introduced to serve as an effective description to the raw trajectory. More importantly, based on the trajectory signature, a probabilistic model-based cluster signature is further developed for modeling a motion class. The cluster signature is a mixture model-based motion description that is useful for motion class perception, recognition and to benefit a generalized robot task representation. The signature modeling process is supported by integrating the EM and IPRA algorithms. The conducted experiments verified the cluster signature's effectiveness.