Sparse representation based action and gesture recognition

Bomma, Sushma; Favaro, Paolo; Robertson, Neil (2013). Sparse representation based action and gesture recognition. In: 20th IEEE International Conference on Image Processing (ICIP) (pp. 141-145). IEEE 10.1109/ICIP.2013.6738030

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In this paper we present a solution to the problem of action and gesture recognition using sparse representations. The dictionary is modelled as a simple concatenation of features computed for each action or gesture class from the training data, and test data is classified by finding sparse representation of the test video features over this dictionary. Our method does not impose any explicit training procedure on the dictionary. We experiment our model with two kinds of features, by projecting (i) Gait Energy Images (GEIs) and (ii) Motion-descriptors, to a lower dimension using Random projection. Experiments have shown 100% recognition rate on standard datasets and are compared to the results obtained with widely used SVM classifier.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	08 Faculty of Science > Institute of Computer Science (INF) 08 Faculty of Science > Institute of Computer Science (INF) > Computer Vision Group (CVG)
UniBE Contributor:	Favaro, Paolo
Subjects:	000 Computer science, knowledge & systems 500 Science > 510 Mathematics
Publisher:	IEEE
Language:	English
Submitter:	Paolo Favaro
Date Deposited:	04 Jun 2014 10:59
Last Modified:	05 Dec 2022 14:31
Publisher DOI:	10.1109/ICIP.2013.6738030
Related URLs:	Publisher
URI:	https://boris.unibe.ch/id/eprint/46701