Mice and larvae tracking using a particle filter with an auto-adjustable observation model

This paper proposes a novel way to combine different observation models in a particle filter framework. This, so called, auto-adjustable observation model, enhance the particle filter accuracy when the tracked objects overlap without infringing a great runtime penalty to the whole tracking system. The approach has been tested under two important real world situations related to animal behavior: mice and larvae tracking. The proposal was compared to some state-of-art approaches and the results show, under the datasets tested, that a good trade-off between accuracy and runtime can be achieved using an auto-adjustable observation model. (C) 2009 Elsevier B.V. All rights reserved.

Dom Bosco Catholic University

Dom Bosco Catholic University

UCDB

UCDB

Foundation of Teaching, Science and Technology Development of Mato Grosso do Sul State

Foundation of Teaching, Science and Technology Development of Mato Grosso do Sul State

FUNDECT

FUNDECT

Brazilian Studies and Projects Funding Body (FINEP)

Financiadora de Estudos e Projetos (FINEP)

Brazilian National Counsel of Technological and Scientific Development, CNPQ

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)