PLATE: A perception-latency aware estimator,
Rodrigo Aldana-López Rosario Aragüés Carlos Sagüés
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.