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

Carlos R. del-Blanco; Fernando Jaureguizar; Narciso García; Luis Salgado

doi:10.1117/12.820203

4 May 2009 Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery

Carlos R. del-Blanco, Fernando Jaureguizar, Narciso García, Luis Salgado

Author Affiliations +

Proceedings Volume 7335, Automatic Target Recognition XIX; 733514 (2009) https://doi.org/10.1117/12.820203
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Carlos R. del-Blanco, Fernando Jaureguizar, Narciso García, and Luis Salgado "Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery", Proc. SPIE 7335, Automatic Target Recognition XIX, 733514 (4 May 2009); https://doi.org/10.1117/12.820203

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available