Abstract: 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.
Keywords: automatic target recognition; joint tracking recognition and segmentation; shape manifolds; level set; manifold learning
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Gong, J.; Fan, G.; Yu, L.; Havlicek, J.P.; Chen, D.; Fan, N. Joint Target Tracking, Recognition and Segmentation for Infrared Imagery Using a Shape Manifold-Based Level Set. Sensors 2014, 14, 10124-10145.
Gong J, Fan G, Yu L, Havlicek JP, Chen D, Fan N. Joint Target Tracking, Recognition and Segmentation for Infrared Imagery Using a Shape Manifold-Based Level Set. Sensors. 2014; 14(6):10124-10145.
Gong, Jiulu; Fan, Guoliang; Yu, Liangjiang; Havlicek, Joseph P.; Chen, Derong; Fan, Ningjun. 2014. "Joint Target Tracking, Recognition and Segmentation for Infrared Imagery Using a Shape Manifold-Based Level Set." Sensors 14, no. 6: 10124-10145.