Title
Probabilistic Cluster Signature For Modeling Motion Classes
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. © 2009 IEEE.
Publication Date
12-11-2009
Publication Title
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Number of Pages
5731-5736
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/IROS.2009.5354142
Copyright Status
Unknown
Socpus ID
76249101837 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/76249101837
STARS Citation
Wu, Shandong; Li, Y. F.; and Zhang, Jianwei, "Probabilistic Cluster Signature For Modeling Motion Classes" (2009). Scopus Export 2000s. 11289.
https://stars.library.ucf.edu/scopus2000/11289