Gravity optimised particle filter for hand tracking

Malik Arman Morshidi

doi:10.1016/j.patcog.2013.06.032

2014-01-01

Malik Arman Morshidi

Tardi Tjahjadi *

DOI：10.1016/j.patcog.2013.06.032

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摘要

En 中文

This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles. (C) 2013 Elsevier Ltd. All rights reserved.

Keyword:

Particle filter

Articulated hand tracking

Finger movement

Gravity

Convexity defects

CamShift

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期刊

Pattern Recognition

IF:

7.6

论文数:

1.3W

被引数:

4.5W

机构

University of Warwick

学者数:

2.2W

论文数: 2.2W

被引数: 85

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