Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/104651
Title: Visual tracking with generative template model based on riemannian manifold of covariances
Authors: Chen, M.
Pang, S.K.
Cham, T.J.
Goh, A. 
Keywords: Generative template model
Particle filtering
Riemannian manifolds
Template update
Tracking
Issue Date: 2011
Citation: Chen, M.,Pang, S.K.,Cham, T.J.,Goh, A. (2011). Visual tracking with generative template model based on riemannian manifold of covariances. Fusion 2011 - 14th International Conference on Information Fusion : -. ScholarBank@NUS Repository.
Abstract: Robust visual tracking is a research area that has many important applications. The main challenges include how the target image can be modeled and how this model can be updated. In this paper, we model the target using a covariance descriptor. This descriptor is robust to problems that commonly occur in visual tracking such as pixel-pixel misalignment, pose and illumination changes. We model the changes in the template using a generative process. We introduce a new dynamical model for the template update using a random walk on the Riemannian manifold where the covariance descriptors lie in. This enables us to jointly quantify the uncertainties relating to the kinematic states and the template in a principled way. The sequential inference of the posterior distribution of the kinematic states and the template is done using a particle filter. Our results show that this principled approach is robust to changes in illumination, pose and spatial affine transformation. © 2011 IEEE.
Source Title: Fusion 2011 - 14th International Conference on Information Fusion
URI: http://scholarbank.nus.edu.sg/handle/10635/104651
ISBN: 9781457702679
Appears in Collections:Staff Publications

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