Joint target tracking, recognition and segmentation for infrared imagery using a shape manifold-based level set

We propose a new integrated target tracking, recognition and segmentation algorithm, called ATR-Seg, for infrared imagery. ATR-Seg is formulated in a probabilistic shape-aware level set framework that incorporates a joint view-identity manifold (JVIM) for target shape modeling. As a shape generative...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2014-06, Vol.14 (6), p.10124-10145
Main Authors: Gong, Jiulu, Fan, Guoliang, Yu, Liangjiang, Havlicek, Joseph P, Chen, Derong, Fan, Ningjun
Format: Article
Language:English
Subjects:
Algorithms
automatic target recognition
Byproducts
Computer engineering
Distance learning
Infrared imagery
joint tracking recognition and segmentation
level set
manifold learning
Manifolds
Matching
Object recognition
Segmentation
Sensors
shape manifolds
Shape recognition
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Summary:We propose a new integrated target tracking, recognition and segmentation algorithm, called ATR-Seg, for infrared imagery. ATR-Seg is formulated in a probabilistic shape-aware level set framework that incorporates a joint view-identity manifold (JVIM) for target shape modeling. As a shape generative model, JVIM features a unified manifold structure in the latent space that is embedded with one view-independent identity manifold and infinite identity-dependent view manifolds. In the ATR-Seg algorithm, the ATR problem formulated as a sequential level-set optimization process over the latent space of JVIM, so that tracking and recognition can be jointly optimized via implicit shape matching where target segmentation is achieved as a by-product without any pre-processing or feature extraction. Experimental results on the recently released SENSIAC ATR database demonstrate the advantages and effectiveness of ATR-Seg over two recent ATR algorithms that involve explicit shape matching.
ISSN:1424-8220
1424-8220
DOI:10.3390/s140610124