Discriminative Sequence Back-constrained GP-LVM for MOCAP based Action Recognition

Abstract : In this paper we address the problem of human action recognition within Motion Capture sequences. We introduce a method based on Gaussian Process Latent Variable Models and Alignment Kernels. We build a new discriminative latent variable model with back-constraints induced by the similarity of the original sequences. We compare the proposed method with a standard sequence classification method based on Dynamic Time Warping and with the recently introduced V-GPDS model which is able to model highly dimensional dynamical systems. The proposed methodology exhibits high performance even for datasets that have not been manually pre-processed while it further allows fast inference by exploiting the back constraints.
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Communication dans un congrès
International Conference on Pattern Recognition Applications and Methods, Feb 2013, Barcelona, Spain. 2013, 〈10.5220/0004268600870096〉
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Valsamis Ntouskos, Panagiotis Papadakis, Fiora Pirri. Discriminative Sequence Back-constrained GP-LVM for MOCAP based Action Recognition. International Conference on Pattern Recognition Applications and Methods, Feb 2013, Barcelona, Spain. 2013, 〈10.5220/0004268600870096〉. 〈hal-00832895〉

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