000129653 001__ 129653 000129653 005__ 20241125101133.0 000129653 0247_ $$2doi$$a10.1016/j.isatra.2023.08.013 000129653 0248_ $$2sideral$$a136053 000129653 037__ $$aART-2023-136053 000129653 041__ $$aeng 000129653 100__ $$aAldana-López, Rodrigo$$uUniversidad de Zaragoza 000129653 245__ $$aPLATE: A perception-latency aware estimator 000129653 260__ $$c2023 000129653 5060_ $$aAccess copy available to the general public$$fUnrestricted 000129653 5203_ $$aTarget tracking is a popular problem with many potential applications. There has been a lot of effort on improving the quality of the detection of targets using cameras through different techniques. In general, with higher computational effort applied, i.e., a longer perception-latency, a better detection accuracy is obtained. However, it is not always useful to apply the longest perception-latency allowed, particularly when the environment does not require to and when the computational resources are shared between other tasks. In this work, we propose a new Perception-LATency aware Estimator (PLATE), which uses different perception configurations in different moments of time in order to optimize a certain performance measure. This measure takes into account a perception-latency and accuracy trade-off aiming for a good compromise between quality and resource usage. Compared to other heuristic frame-skipping techniques, PLATE comes with a formal complexity and optimality analysis. The advantages of PLATE are verified by several experiments including an evaluation over a standard benchmark with real data and using state of the art deep learning object detection methods for the perception stage. 000129653 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000129653 590__ $$a6.3$$b2023 000129653 592__ $$a1.572$$b2023 000129653 591__ $$aAUTOMATION & CONTROL SYSTEMS$$b11 / 84 = 0.131$$c2023$$dQ1$$eT1 000129653 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b8 / 181 = 0.044$$c2023$$dQ1$$eT1 000129653 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b6 / 76 = 0.079$$c2023$$dQ1$$eT1 000129653 593__ $$aApplied Mathematics$$c2023$$dQ1 000129653 593__ $$aElectrical and Electronic Engineering$$c2023$$dQ1 000129653 593__ $$aInstrumentation$$c2023$$dQ1 000129653 593__ $$aControl and Systems Engineering$$c2023$$dQ1 000129653 593__ $$aComputer Science Applications$$c2023$$dQ1 000129653 594__ $$a11.7$$b2023 000129653 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000129653 700__ $$0(orcid)0000-0001-9458-6257$$aAragüés, Rosario$$uUniversidad de Zaragoza 000129653 700__ $$0(orcid)0000-0002-3032-954X$$aSagüés, Carlos$$uUniversidad de Zaragoza 000129653 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000129653 773__ $$g142 (2023), 716-730$$pISA trans.$$tISA TRANSACTIONS$$x0019-0578 000129653 8564_ $$s1414322$$uhttps://zaguan.unizar.es/record/129653/files/texto_completo.pdf$$yVersión publicada 000129653 8564_ $$s2993463$$uhttps://zaguan.unizar.es/record/129653/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000129653 909CO $$ooai:zaguan.unizar.es:129653$$particulos$$pdriver 000129653 951__ $$a2024-11-22-11:59:36 000129653 980__ $$aARTICLE