Improving head and body pose estimation through semi-supervised manifold alignment

Alexandre Heili, Jagannadan Varadarajan, Bernard Ghanem, Narendra Ahuja, Jean-Marc Odobez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


In this paper, we explore the use of a semi-supervised manifold alignment method for domain adaptation in the context of human body and head pose estimation in videos. We build upon an existing state-of-the-art system that leverages on external labelled datasets for the body and head features, and on the unlabelled test data with weak velocity labels to do a coupled estimation of the body and head pose. While this previous approach showed promising results, the learning of the underlying manifold structure of the features in the train and target data and the need to align them were not explored despite the fact that the pose features between two datasets may vary according to the scene, e.g. due to different camera point of view or perspective. In this paper, we propose to use a semi-supervised manifold alignment method to bring the train and target samples closer within the resulting embedded space. To this end, we consider an adaptation set from the target data and rely on (weak) labels, given for example by the velocity direction whenever they are reliable. These labels, along with the training labels are used to bias the manifold distance within each manifold and to establish correspondences for alignment.
Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Image Processing (ICIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Print)9781479957514
StatePublished - Feb 5 2015

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: IEEE TERMS



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