venkatesh-humanaction-2004.pdf (436.31 kB)
Human action segmentation via controlled use of missing data in HMMs
conference contribution
posted on 2004-01-01, 00:00 authored by P Peursum, H Bui, Svetha VenkateshSvetha Venkatesh, G WestSegmentation of individual actions from a stream of human motion is an open problem in computer vision. This paper approaches the problem of segmenting higher-level activities into their component sub-actions using Hidden Markov Models modified to handle missing data in the observation vector. By controlling the use of missing data, action labels can be inferred from the observation vector during inferencing, thus performing segmentation and classification simultaneously. The approach is able to segment both prominent and subtle actions, even when subtle actions are grouped together. The advantage of this method over sliding windows and Viterbi state sequence interrogation is that segmentation is performed as a trainable task, and the temporal relationship between actions is encoded in the model and used as evidence for action labelling.
History
Event
International Conference on Pattern Recognition (17th : 2004 : Cambridge, U. K.)Pagination
440 - 445Publisher
IEEELocation
Cambridge, U. K.Place of publication
Washington, D. C.Start date
2004-08-23End date
2004-08-26ISSN
1051-4651ISBN-10
0769521282Language
engNotes
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Publication classification
E1.1 Full written paper - refereedCopyright notice
2004, IEEEEditor/Contributor(s)
J Kittler, M Petrou, M NixonTitle of proceedings
ICPR 2004 : Proceedings of the 17th International Conference on Pattern RecognitionUsage metrics
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