doi:10.1016/j.isatra.2023.08.013engAldana-López, RodrigoAragüés, RosarioSagüés, CarlosPLATE: A perception-latency aware estimatorART-2023-136053Target 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.2023http://zaguan.unizar.es/record/12965310.1016/j.isatra.2023.08.013http://zaguan.unizar.es/record/129653oai:zaguan.unizar.es:129653ISA TRANSACTIONS 142 (2023), 716-730by-nc-ndhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esinfo:eu-repo/semantics/openAccess