Appearance learning by adaptive Kalman filters for FLIR tracking

Venkataraman, V.; Fan, Guoliang; Fan, Xiaohui; Havlicek, J.P.

doi:10.1109/cvpr.2009.5205206

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…-automatic target recognition (ATR) is another area where AI can be used in the military. Defined as "ability for an algorithm or device to recognize targets or other objects based on data obtained from sensors" [3], the use of AI in this field can start from simpler tasks, such as the accurate target identification for unmanned aerial vehicles and cruise missiles. Currently, the activity of monitoring threats and obtaining an accurate picture of the combat environment is largely based on intelligence, surveillance and reconnaissance (SRI) operations and this is probably not a factor that is going to change in the future operational environment.…”

Section: The Uses Of Artificial Intelligence In the Militarymentioning

confidence: 99%

Artificial Intelligence and the Future of Defence Planning and Resources Management

Constantinescu

Dumitrache

2022

International Conference KNOWLEDGE-BASED ORGANIZATION

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The concept of artificial intelligence (AI) is rapidly becoming a significant factor for change, in the society in general, both in the private and public sectors. It has not been ignored by the military either, as countries throughout the world (from the US to Russia and China) are studying the potential uses of artificial intelligence in a functional context, and its uses in the defense area. The purpose of this paper is to explore the future potential uses and limitations of using the artificial intelligence in the military, with emphasis on the areas of defence planning and defence resources management.

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Section: The Uses Of Artificial Intelligence In the Militarymentioning

confidence: 99%

Artificial Intelligence and the Future of Defence Planning and Resources Management

Constantinescu

Dumitrache

2022

International Conference KNOWLEDGE-BASED ORGANIZATION

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“…In [5] the target is represented by intensity and shape features and a self-organizing map is used for classification. Histogram-based representations were shown to be simple yet robust under difficult tracking conditions [4,6], but such representations cannot effectively discriminate among different target types due to the lack of higher order structure. In [7], the shape variability due to different structures and poses is characterized explicitly using a deformable and parametric model that must be optimized for localization and recognition.…”

Section: Introductionmentioning

confidence: 99%

Automated target tracking and recognition using coupled view and identity manifolds for shape representation

Venkataraman

Fan

et al. 2011

EURASIP J. Adv. Signal Process.

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We propose a new couplet of identity and view manifolds for multi-view shape modeling that is applied to automated target tracking and recognition (ATR). The identity manifold captures both inter-class and intra-class variability of target shapes, while a hemispherical view manifold is involved to account for the variability of viewpoints. Combining these two manifolds via a non-linear tensor decomposition gives rise to a new target generative model that can be learned from a small training set. Not only can this model deal with arbitrary view/ pose variations by traveling along the view manifold, it can also interpolate the shape of an unknown target along the identity manifold. The proposed model is tested against the recently released SENSIAC ATR database and the experimental results validate its efficacy both qualitatively and quantitatively.

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Appearance learning by adaptive Kalman filters for FLIR tracking

Abstract: Abstract

Cited by 2 publications

References 19 publications

Artificial Intelligence and the Future of Defence Planning and Resources Management

Artificial Intelligence and the Future of Defence Planning and Resources Management

Automated target tracking and recognition using coupled view and identity manifolds for shape representation

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