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

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

Description

Title: Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery
Author/s:
  • Blanco Adán, Carlos Roberto del
  • Jaureguizar Núñez, Fernando
  • García Santos, Narciso
  • Salgado Álvarez de Sotomayor, Luis
Item Type: Presentation at Congress or Conference (Article)
Event Title: SPIE Automatic Target Recognition XIX
Event Dates: 13 de Abril del 2009
Event Location: Orlando, Florida, EEUU
Title of Book: SPIE Automatic Target Recognition XIX
Date: 2009
ISBN: 9780819476012
Volume: 7335
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (888kB) | Preview

Abstract

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.

More information

Item ID: 7268
DC Identifier: http://oa.upm.es/7268/
OAI Identifier: oai:oa.upm.es:7268
Official URL: http://spiedigitallibrary.org/proceedings/resource/2/psisdg/7335/1/733514_1?isAuthorized=no
Deposited by: Doctor Carlos Roberto del Blanco Adán
Deposited on: 27 May 2011 11:50
Last Modified: 20 Apr 2016 16:27
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM