{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:28:39Z","timestamp":1760243319985,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2014,8,4]],"date-time":"2014-08-04T00:00:00Z","timestamp":1407110400000},"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>One of the main challenges in automatic target tracking applications is represented by the need to maintain a low computational footprint, especially when dealing with real-time scenarios and the limited resources of embedded environments. In this context, significant results can be obtained by using forward-looking infrared sensors capable of providing distinctive features for targets of interest. In fact, due to their nature, forward-looking infrared (FLIR) images lend themselves to being used with extremely small footprint techniques based on the extraction of target intensity profiles. This work proposes a method for increasing the computational efficiency of template-based target tracking algorithms. In particular, the speed of the algorithm is improved by using a dynamic threshold that narrows the number of computations, thus reducing both execution time and resources usage. The proposed approach has been tested on several datasets, and it has been compared to several target tracking techniques. Gathered results, both in terms of theoretical analysis and experimental data, showed that the proposed approach is able to achieve the same robustness of reference algorithms by reducing the number of operations needed and the processing time.<\/jats:p>","DOI":"10.3390\/s140814106","type":"journal-article","created":{"date-parts":[[2014,8,5]],"date-time":"2014-08-05T10:59:37Z","timestamp":1407236377000},"page":"14106-14130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Relevance-Based Template Matching for Tracking Targets in FLIR Imagery"],"prefix":"10.3390","volume":"14","author":[{"given":"Gianluca","family":"Paravati","sequence":"first","affiliation":[{"name":"Politecnico di Torino, Dipartimento di Automatica e Informatica, Corso Duca degli Abruzzi 24,10129 Torino, Italy"}]},{"given":"Stefano","family":"Esposito","sequence":"additional","affiliation":[{"name":"Politecnico di Torino, Dipartimento di Automatica e Informatica, Corso Duca degli Abruzzi 24,10129 Torino, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2014,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s00138-007-0078-y","article-title":"Thermo-visual feature fusion for object tracking using multiple spatiogram trackers","volume":"19","author":"Conaire","year":"2008","journal-title":"Mach. Vis. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1109\/TAES.2009.5089552","article-title":"A Novel Ego-Motion Compensation Strategy for Automatic Target Tracking in FLIR Video Sequences taken from UAVs","volume":"45","author":"Sanna","year":"2009","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Paravati, G., Pralio, B., Sanna, A., and Lamberti, F. (2011, January 9\u201312). A reconfigurable multi-touch remote control system for teleoperated robots. Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2011.5722512"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/TII.2009.2017523","article-title":"Time-driven access and forwarding for industrial wireless multihop networks","volume":"5","author":"Baldi","year":"2009","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4092","DOI":"10.1016\/j.comnet.2007.04.019","article-title":"A scalable solution for engineering streaming traffic in the future Internet","volume":"51","author":"Baldi","year":"2007","journal-title":"Comput. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1364\/JOCN.3.000447","article-title":"Scalable fractional lambda switching: A testbed","volume":"3","author":"Baldi","year":"2011","journal-title":"J. Opt. Commun. Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1007\/s00138-011-0336-x","article-title":"Vehicle detection and tracking in airborne videos by multi-motion layer analysis","volume":"23","author":"Cao","year":"2012","journal-title":"Mach. Vis. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/TCSVT.2009.2031395","article-title":"Object tracking in structured environments for video surveillance applications","volume":"20","author":"Zhu","year":"2010","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1109\/TCSVT.2013.2283646","article-title":"Part-Based Online Tracking With Geometry Constraint and Attention Selection","volume":"24","author":"Fang","year":"2014","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1109\/TIP.2014.2300823","article-title":"Robust Superpixel Tracking","volume":"23","author":"Yang","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1109\/LSP.2014.2320291","article-title":"Object Tracking via Robust Multitask Sparse Representation","volume":"21","author":"Bai","year":"2014","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1016\/j.patcog.2012.10.006","article-title":"Sparse coding based visual tracking: Review and experimental comparison","volume":"46","author":"Zhang","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.neucom.2011.11.031","article-title":"Robust visual tracking based on online learning sparse representation","volume":"100","author":"Zhang","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2337542.2337560","article-title":"Robust Visual Tracking Using an Effective Appearance Model Based on Sparse Coding","volume":"3","author":"Zhang","year":"2012","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/TIP.2005.860626","article-title":"Target tracking in infrared imagery using weighted composite reference function-based decision fusion","volume":"15","author":"Dawoud","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1117\/1.1789982","article-title":"Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators","volume":"13","author":"Choudhary","year":"2004","journal-title":"J. Electron. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1109\/TIM.2005.855090","article-title":"Automatic Target Tracking in FLIR Image Sequences Using Intensity Variation Function and Template Modeling","volume":"54","author":"Alam","year":"2005","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1109\/TAES.2011.5751271","article-title":"Improving Robustness of Infrared Target Tracking Algorithms Based on Template Matching","volume":"47","author":"Lamberti","year":"2011","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.1109\/TIM.2009.2017665","article-title":"A Genetic Algorithm for Target Tracking in FLIR Video Sequences Using Intensity Variation Function","volume":"58","author":"Paravati","year":"2009","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","unstructured":"Collins, R.T. (2003, January 18\u201320). Mean-shift blob tracking through scale space. Madison, WI, USA."},{"key":"ref_21","unstructured":"Yilmaz, A., Shafique, K., Lobo, N., Li, X., Olson, T., and Shah, M.A. (2001, January 14). Target-tracking in FLIR imagery using mean-shift and global motion compensation. Kauai, HI, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/S0262-8856(03)00059-3","article-title":"Tracking in airborne forward looking infrared imagery","volume":"21","author":"Yilmaz","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_23","unstructured":"Paravati, G., Sanna, A., and Lamberti, F. (2011, January 24\u201326). An image feature descriptors-based recovery activation metric for FLIR target tracking. Rome, Italy."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.cviu.2006.06.010","article-title":"IEEE OTCBVS WS Series Bench; Background-Subtraction Using Contour-Based Fusion of Thermal and Visible Imagery","volume":"106","author":"Davis","year":"2007","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., and Yang, M.H. (2013, January 23\u201328). Online object tracking: A benchmark. Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Conaire, C.O., O'Connor, N.E., Cooke, E., and Smeaton, A.F. (2006, January 10\u201313). Comparison of fusion methods for thermo-visual surveillance tracking. Florence, Italy.","DOI":"10.1109\/ICIF.2006.301618"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.OE.53.2.023105","article-title":"IVF3: Exploiting Intensity Variation Function for high performance pedestrian tracking in FLIR imagery","volume":"53","author":"Lamberti","year":"2014","journal-title":"Opt. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/TPAMI.2003.1195991","article-title":"Kernel-based object tracking","volume":"25","author":"Comaniciu","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Henriques, J., Caseiro, R., Martins, P., and Batista, J. (2012, January 7\u201313). Exploiting the circulant structure of tracking by detection with kernels. Florence, Italy.","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Perez, P., Hue, C., Vermaak, J., and Gangnet, M. (2002, January 28\u201331). Color-based probabilistic tracking. Copenhagen, Denmark.","DOI":"10.1007\/3-540-47969-4_44"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1109\/TPAMI.2005.205","article-title":"Online selection of discriminative tracking features","volume":"27","author":"Collins","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/8\/14106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:14:21Z","timestamp":1760217261000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/8\/14106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,8,4]]},"references-count":31,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2014,8]]}},"alternative-id":["s140814106"],"URL":"https:\/\/doi.org\/10.3390\/s140814106","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2014,8,4]]}}}