PLATE: A perception-latency aware estimator,

• URL: http://arxiv.org/abs/2401.13596v1
• Date: Wed, 24 Jan 2024 17:04:18 GMT
• Title: PLATE: A perception-latency aware estimator,
• Authors: Rodrigo Aldana-L\'opez, Rosario Arag\"u\'es, Carlos Sag\"u\'es
• Abstract summary: Perception-LATency aware Estorimator (PLATE) PLATE uses different perception configurations in different moments of time in order to optimize a certain performance measure. Compared to other frame-skipping techniques, PLATE comes with a formal complexity and optimality analysis.
• Score: 0.46040036610482665
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Target 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 doesn't 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.

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