NDLI: Visual tracking using region covariance and geometric particle filtering

Content Provider	IEEE Xplore Digital Library
Author	Daqing Chen Jiuqiang Han Zhijian Yu
Copyright Year	2010
Description	Author affiliation: Taiyuan Satellite Launch Center, Taiyuan, 030027, China (Zhijian Yu) \|\| School of Electronics and Information Engineering, Xi′an Jiaotong University, Xi′an, 710049, China (Daqing Chen; Jiuqiang Han)
Abstract	Region covariance descriptor recently proposed has been approved robust and elegant to describe a region of interest, which has been applied to visual tracking. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties as well as their correlation are characterized. The similarity of two covariance descriptor is measured on Riemannian manifolds. Within a probabilistic framework, we integrate covariance descriptor into Monte Carlo tracking technique for visual tracking. Most existing particle filtering based tracking algorithms treat deformation parameters of the target as a vector. We have proposed a visual tracking algorithm using particle filtering on the affine group, which implements the geometric particle filter with the constraint that the system state lies in a low dimensional manifold: affine lie group. The sequential Bayesian updating consists in drawing state samples while moving on the manifold geodesics; The Region covariance is updated using a novel approach in a Riemannian space. Theoretic analysis and experimental evaluations against the tracking algorithm based on geometric particle filtering demonstrate the promise and effectiveness of this algorithm.
Starting Page	381
Ending Page	386
File Size	902942
Page Count	6
File Format	PDF
ISBN	9781424465132
e-ISBN	9781424465163
DOI	10.1109/CISP.2010.5648015
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Algorithm design and analysis Visualization Region covariance Target tracking Filtering visual tracking geometric particle filtering Covariance matrix Mathematical model Lie Group
Content Type	Text
Resource Type	Article

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