{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:27:41Z","timestamp":1760243261188,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2014,6,10]],"date-time":"2014-06-10T00:00:00Z","timestamp":1402358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s140610124","type":"journal-article","created":{"date-parts":[[2014,6,10]],"date-time":"2014-06-10T11:50:51Z","timestamp":1402401051000},"page":"10124-10145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Joint Target Tracking, Recognition and Segmentation for Infrared Imagery Using a Shape Manifold-Based Level Set"],"prefix":"10.3390","volume":"14","author":[{"given":"Jiulu","family":"Gong","sequence":"first","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Oklahoma State University, 202 Engineering South, Stillwater, OK 74078, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangjiang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Oklahoma State University, 202 Engineering South, Stillwater, OK 74078, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph","family":"Havlicek","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Oklahoma, 110 West Boyd, DEH 150Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Derong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningjun","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1177352.1177355","article-title":"Object tracking: A survey","volume":"38","author":"Yilmaz","year":"2006","journal-title":"ACM Comput. Surv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1109\/TAES.2010.5461646","article-title":"Generative Models for Maneuvering Target Tracking","volume":"46","author":"Fan","year":"2010","journal-title":"IEEE Trans. Aerospace Electron. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1117\/12.395067","article-title":"Bayesian filtering for tracking pose and location of rigid targets","volume":"4052","author":"Srivastava","year":"2000","journal-title":"Proc. SPIE"},{"key":"ref_4","unstructured":"Shaik, J., and Iftekharuddin, K. (2003, January 20\u201324). Automated tracking and classification of infrared images. Portland, OR, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Khan, Z., and Gu, I.H. (2011, January 6\u201313). Tracking visual and infrared objects using joint Riemannian manifold appearance and affine shape modeling. Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130473"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Prisacariu, V., and Reid, I. (2011, January 20\u201325). Nonlinear shape manifolds as shape priors in level set segmentation and tracking. Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995687"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Prisacariu, V., and Reid, I. (2011, January 6\u201313). Shared shape spaces. Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126547"},{"key":"ref_8","first-page":"1","article-title":"Automated Target Tracking and Recognition using Coupled View and Identity Manifolds for Shape Representation","volume":"124","author":"Venkataraman","year":"2011","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Urtasun, R., Fleet, D.J., Geiger, A., Popovi\u0107, J., Darrell, T.J., and Lawrence, N.D. (2008, January 5\u20139). Topologically-constrained latent variable models. Helsinki, Finland.","DOI":"10.1145\/1390156.1390292"},{"key":"ref_10","unstructured":"Yao, A., Gall, J., Gool, L., and Urtasun, R. (2011, January 12\u201314). Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities. Granada, Spain."},{"key":"ref_11","unstructured":"Military Sensing Information Analysis Center (SENSIAC). Available online: https:\/\/www.sensiac.org\/."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yankov, D., and Keogh, E. (2006, January 18\u201322). Manifold Clustering of Shapes. Hong Kong, China.","DOI":"10.1109\/ICDM.2006.101"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality Reduction by Local Linear Embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Etyngier, P., Segonne, F., and Keriven, R. (2007, January 14\u201320). Shape Priors using Manifold Learning Techniques. Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4409040"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Etyngier, P., Keriven, R., and Segonne, F. (2007, January 16\u201319). Projection onto a Shape Manifold for Image Segmentation with Prior. San Antonio, TX, USA.","DOI":"10.1109\/ICIP.2007.4380029"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, R., Lei, Z., Yuan, X., and Li, S. (2008, January 17\u201319). Regularized active shape model for shape alignment. Amsterdam, the Netherlands.","DOI":"10.1109\/AFGR.2008.4813423"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"L\u00fcthi, M., Albrecht, T., and Vetter, T. (2009, January 7\u20139). Probabilistic Modeling and Visualization of the Flexibility in Morphable Models. York, UK.","DOI":"10.1007\/978-3-642-03596-8_14"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1109\/TPAMI.2007.70774","article-title":"A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors","volume":"30","author":"Dambreville","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","first-page":"1783","article-title":"Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models","volume":"6","author":"Lawrence","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref_20","unstructured":"Elgammal, A., and Lee, C.S. (2, January 27). Separating style and content on a nonlinear manifold. Washington, DC, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, C., and Elgammal, A. (2007, January 14\u201321). Modeling View and Posture Manifolds for Tracking. Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4409030"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/TAES.2003.1261132","article-title":"Survey of maneuvering target tracking. Part I. Dynamic models","volume":"39","author":"Li","year":"2003","journal-title":"IEEE Trans. Aerospace Electron. Syst."},{"key":"ref_23","unstructured":"Gong, J., Fan, G., Yu, L., Havlicek, J., and Chen, D. (3, January 30). Joint view-identity manifold for target tracking and recognition. Orlando, FL, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.cviu.2013.10.002","article-title":"Joint view-identity manifold for infrared target tracking and recognition","volume":"118","author":"Gong","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Urtasun, R., and Darrell, T. (2008, January 23\u201328). Sparse probabilistic regression for activity-independent human pose inference. Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587360"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.cviu.2013.10.002","article-title":"Joint View-Identity Manifold for Infrared Target Tracking and Recognition","volume":"118","author":"Gong","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bibby, C., and Reid, I. (2008, January 12\u201318). Robust Real-Time Visual Tracking Using Pixel-Wise Posteriors. Marseille, France.","DOI":"10.1007\/978-3-540-88688-4_61"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gong, J., Fan, G., Havlicek, J.P., Fan, N., and Chen, D. (2013, January 15\u201318). Infrared Target Tracking Recognition Segmentation using Shape-Aware Level Set. Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738676"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/78.978374","article-title":"A Tutorial on Particle Filters for Online Non-linear\/Non-Gaussian Bayesian Tracking","volume":"50","author":"Arulampalam","year":"2002","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.patrec.2005.11.005","article-title":"Efficient adaptive density estimation per image pixel for the task of background subtraction","volume":"27","author":"Zivkovic","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4622","DOI":"10.1109\/TIP.2012.2210233","article-title":"Adaptive Kalman Filtering for Histogram-based Appearance Learning in Infrared Imagery","volume":"21","author":"Venkataraman","year":"2012","journal-title":"IEEE Trans. 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