Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery

Blanco Adán, Carlos Roberto del; Jaureguizar Núñez, Fernando; García Santos, Narciso y Salgado Álvarez de Sotomayor, Luis (2009). Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery. En: "SPIE Automatic Target Recognition XIX", 13 de Abril del 2009, Orlando, Florida, EEUU. ISBN 9780819476012.

Descripción

Título: Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery
Autor/es:
  • Blanco Adán, Carlos Roberto del
  • Jaureguizar Núñez, Fernando
  • García Santos, Narciso
  • Salgado Álvarez de Sotomayor, Luis
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: SPIE Automatic Target Recognition XIX
Fechas del Evento: 13 de Abril del 2009
Lugar del Evento: Orlando, Florida, EEUU
Título del Libro: SPIE Automatic Target Recognition XIX
Fecha: 2009
ISBN: 9780819476012
Volumen: 7335
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Automatic target tracking in airborne FLIR imagery is currently a challenge due to the camera ego-motion. This phenomenon distorts the spatio-temporal correlation of the video sequence, which dramatically reduces the tracking performance. Several works address this problem using ego-motion compensation strategies. They use a deterministic approach to compensate the camera motion assuming a specific model of geometric transformation. However, in real sequences a specific geometric transformation can not accurately describe the camera ego-motion for the whole sequence, and as consequence of this, the performance of the tracking stage can significantly decrease, even completely fail. The optimum transformation for each pair of consecutive frames depends on the relative depth of the elements that compose the scene, and their degree of texturization. In this work, a novel Particle Filter framework is proposed to efficiently manage several hypothesis of geometric transformations: Euclidean, affine, and projective. Each type of transformation is used to compute candidate locations of the object in the current frame. Then, each candidate is evaluated by the measurement model of the Particle Filter using the appearance information. This approach is able to adapt to different camera ego-motion conditions, and thus to satisfactorily perform the tracking. The proposed strategy has been tested on the AMCOM FLIR dataset, showing a high efficiency in the tracking of different types of targets in real working conditions.

Más información

ID de Registro: 7268
Identificador DC: http://oa.upm.es/7268/
Identificador OAI: oai:oa.upm.es:7268
URL Oficial: http://spiedigitallibrary.org/proceedings/resource/2/psisdg/7335/1/733514_1?isAuthorized=no
Depositado por: Doctor Carlos Roberto del Blanco Adán
Depositado el: 27 May 2011 11:50
Ultima Modificación: 20 Abr 2016 16:27
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