Visual Extraction of Motion-Based Information from Image Sequences

David Gibson, A. Sanfeliu, Campbell Neill W., J.J. Villanueva, Colin Dalton, M. Vanrell, Thomas Barry T., R. Alquezar, T. Huang, J. Serra

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


We describe a system which is designed to assist in extracting high-level information from sets or sequences of images. We show that the method of principal components analysis followed by a neural network learning phase is capable of feature extraction or motion tracking, even through occlusion. Given a minimal amount of user direction for the learning phase, a wide range of features can be automatically extracted. Features discussed in this paper include information associated with human head motions and a birds wings during take off. We have quantified the results, for instance showing that with only $25$ out of $424$ frames of hand labelled information a system to track a persons nose can be trained almost as accurately as a human attempting the same task. We demonstrate a system that is powerful, flexible and, above all, easy for non-specialists to use.
Translated title of the contributionVisual Extraction of Motion-Based Information from Image Sequences
Original languageEnglish
Title of host publicationInternational Conference on Pattern Recognition
PublisherIEEE Computer Society
Publication statusPublished - 2000

Bibliographical note

Other page information: 893-896
Conference Proceedings/Title of Journal: International Conference on Pattern Recognition
Other identifier: 1000514


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