Action recognition using Randomised Ferns

Oshin, O, Gilbert, A, Illingworth, J and Bowden, R (2009) Action recognition using Randomised Ferns In: ICCV 2009, 2009-09-27 - 2009-10-04, Kyoto, Japan.

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Abstract

This paper presents a generic method for recognising and localising human actions in video based solely on the distribution of interest points. The use of local interest points has shown promising results in both object and action recognition. While previous methods classify actions based on the appearance and/or motion of these points, we hypothesise that the distribution of interest points alone contains the majority of the discriminatory information. Motivated by its recent success in rapidly detecting 2D interest points, the semi-naive Bayesian classification method of Randomised Ferns is employed. Given a set of interest points within the boundaries of an action, the generic classifier learns the spatial and temporal distributions of those interest points. This is done efficiently by comparing sums of responses of interest points detected within randomly positioned spatio-temporal blocks within the action boundaries. We present results on the largest and most popular human action dataset using a number of interest point detectors, and demostrate that the distribution of interest points alone can perform as well as approaches that rely upon the appearance of the interest points.

Item Type:	Conference or Workshop Item (Conference Paper)
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :	Name Email ORCID Oshin, O Gilbert, A Illingworth, J Bowden, R
Date :	2009
DOI :	10.1109/ICCVW.2009.5457657
Contributors :	Contribution Name Email ORCID http://www.loc.gov/loc.terms/relators/PBL IEEE,
Additional Information :	Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User :	Symplectic Elements
Date Deposited :	31 May 2012 09:24
Last Modified :	31 Oct 2017 14:33
URI:	http://epubs.surrey.ac.uk/id/eprint/531465

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