Robust Tracking in Aerial Imagery Based on an Ego-Motion Bayesian Model

del-Blanco, Carlos R.; Jaureguizar, Fernando; García, Narciso

doi:10.1155/2010/837405

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Both the proposal distribution and the sampling algorithm are critical in the performance of a particle filtering method. A Markov Chain Monte Carlo (MCMC) scheme is used in [11], [12], [13] to draw data association samples from the target proposal distribution. Similarly, a Gibbs sampler is used in [14], [15] to simulate the underlying data association distribution.…”

Section: A Data Association Techniquesmentioning

confidence: 99%

Inference of Complex Trajectories by Means of a Multibehavior and Multiobject Tracking Algorithm

del-Blanco

Jaureguizar

García

2013

IEEE Trans. Circuits Syst. Video Technol.

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Visual tracking of multiple objects is a fundamental part in many video based systems. Nowadays, there are reliable algorithms that can track a small number of objects in restricted situations. However, the tracking of a large number of objects in uncontrolled situations involving interacting objects with complex dynamics is still a challenge. In this situation, the typical assumptions of linearity and independence of object motions is not fulfilled, causing a low tracking performance. This paper proposes a novel Bayesian tracking algorithm for interacting objects that is able to reliably simulate several object behaviors with an uncalibrated camera, which can be positioned in an arbitrary perspective. Three different models of object behavior are used to simulate and predict the object dynamics, where the proportion of hypotheses of each possible behavior of an object depends on the dynamics (position, velocity, etc.) of the other objects in the scene. Experimental results on public databases prove the reliability and robustness of the proposed tracking algorithm in presence of object interactions.

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Section: A Data Association Techniquesmentioning

confidence: 99%

Inference of Complex Trajectories by Means of a Multibehavior and Multiobject Tracking Algorithm

del-Blanco

Jaureguizar

García

2013

IEEE Trans. Circuits Syst. Video Technol.

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“…In practice, the performance of the particle filtering techniques depends on the ability to correctly sample association hypotheses from a proposal distribution. In [12], a Gibbs sampler is used to sample the data association hypotheses, while in [13,14] a strategy based on a Markov Chain Monte Carlo (MCMC) is followed. The main problem with these samplers is that they are iterative methods that need an unknown number of iterations to converge.…”

Section: State Of the Artmentioning

confidence: 99%

An advanced Bayesian model for the visual tracking of multiple interacting objects

del-Blanco

Jaureguizar

García

2011

EURASIP J. Adv. Signal Process.

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Visual tracking of multiple objects is a key component of many visual-based systems. While there are reliable algorithms for tracking a single object in constrained scenarios, the object tracking is still a challenge in uncontrolled situations involving multiple interacting objects that have a complex dynamics. In this article, a novel Bayesian model for tracking multiple interacting objects in unrestricted situations is proposed. This is accomplished by means of an advanced object dynamic model that predicts possible interactive behaviors, which in turn depend on the inference of potential events of object occlusion. The proposed tracking model can also handle false and missing detections that are typical from visual object detectors operating in uncontrolled scenarios. On the other hand, a Rao-Blackwellization technique has been used to improve the accuracy of the estimated object trajectories, which is a fundamental aspect in the tracking of multiple objects due to its high dimensionality. Excellent results have been obtained using a publicly available database, proving the efficiency of the proposed approach.

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“…However, large displacements between consecutive image frames can occur in some applications. 17,18 To deal with the motion constraint of the previous approaches, 15,16 we propose a new SBNUC method that estimates the correction parameters using several image frames. In the proposed method, we utilize the prior information on the parameters regarding the responsivity and the true scene irradiance.…”

Section: Introductionmentioning

confidence: 99%

Regularization approach to scene-based nonuniformity correction

Kim

et al. 2014

Opt. Eng

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Abstract. Various scene-based nonuniformity correction (SBNUC) methods have been proposed to diminish the residual nonuniformity (RNU) of the infrared focal plane array (IRFPA) sensors. Most existing SBNUC techniques require a relatively large number of image frames to reduce the RNU. In some applications, however, there is not enough time for capturing a large number of image frames prior to the camera operation, or only several image frames are available to users. A new scene-based approach that can correct the RNU using only several image frames is proposed. The proposed method formulates the SBNUC process as an energy minimization problem. In the proposed energy function, we introduce regularization terms for the parameter regarding the responsivity of the IRFPA as well as for the true scene irradiance. Correction results are obtained by minimizing the energy function using a numerical technique. Experimental results demonstrate the effectiveness of the proposed method. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Robust Tracking in Aerial Imagery Based on an Ego-Motion Bayesian Model

Cited by 6 publications

References 30 publications

Inference of Complex Trajectories by Means of a Multibehavior and Multiobject Tracking Algorithm

Inference of Complex Trajectories by Means of a Multibehavior and Multiobject Tracking Algorithm

An advanced Bayesian model for the visual tracking of multiple interacting objects

Regularization approach to scene-based nonuniformity correction

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