Title
Probabilistic Discriminative Dimensionality Reduction for Pose-Based Action Recognition.
Abstract
We examine the problem of classifying action sequences given a small set of examples for each type of action. Based on the presumption that human motion resides in a low dimensional space, we introduce a probabilistic dimensionality reduction model able to recover the structure of a low-dimensional manifold where all the involved actions reside. Requiring that sequences of the same action are placed apart from other sequences, we are able to achieve higher classification rates, with respect to other commonly used techniques, by performing the classification on this manifold. The main contribution is the introduction of a new model, based on Back-constrained GP-LVM which can be used for the efficient classification of sequences. We compare our method with the classification based on the Dynamic Time Warping distance and with the V-GPDS model, adapted for classification. Results are provided for sequences taken from two publicly available datasets which highlight different aspects of the method.
Year
DOI
Venue
2013
10.1007/978-3-319-12610-4_9
PATTERN RECOGNITION APPLICATIONS AND METHODS, ICPRAM 2013
Keywords
Field
DocType
Action recognition,Dimensionality reduction,Manifold learning,Time series models,Motion capture
Motion capture,Dimensionality reduction,Dynamic time warping,Pattern recognition,Computer science,Artificial intelligence,Probabilistic logic,Nonlinear dimensionality reduction,Discriminative model,Small set,Manifold
Conference
Volume
ISSN
Citations 
318
2194-5357
1
PageRank 
References 
Authors
0.35
25
3
Name
Order
Citations
PageRank
Valsamis Ntouskos1125.42
Panagiotis Papadakis237817.19
Fiora Pirri368494.09