Joint Target Tracking, Recognition and Segmentation for Infrared Imagery Using a Shape Manifold-Based Level Set

Sensors

Jiulu Gong¹  Guoliang Fan²  Liangjiang Yu²  Joseph P. Havlicek³  Derong Chen¹ 

[1] School of Mechatronical Engineering, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China; E-Mails:;School of Electrical and Computer Engineering, Oklahoma State University, 202 Engineering South, Stillwater, OK 74078, USA; E-Mail:;School of Electrical and Computer Engineering, University of Oklahoma, 110 West Boyd, DEH 150 Norman, OK 73019, USA; E-Mail:

关键词: automatic target recognition;    joint tracking recognition and segmentation;    shape manifolds;    level set;    manifold learning;   

DOI  :  10.3390/s140610124

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.