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.
This is an open access article distributed under the
Creative Commons Attribution License which permits unrestricted use, distribution,
and reproduction in any medium, provided the original work is properly cited.
Export to BibTeX
MDPI and ACS Style
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.