# PLATE: A perception-latency aware estimator,

arXiv: https://arxiv.org/abs/2401.13596 · HTML: https://mlsanity.com/paper/2401.13596/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [A General Framework for Uncertainty Estimation in Deep Learning](https://mlsanity.com/paper/1907.06890/) (2019)
- [DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM](https://mlsanity.com/paper/2010.07820/) (2020)
- [Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection](https://mlsanity.com/paper/1912.03538/) (2019)
- [Detect or Track: Towards Cost-Effective Video Object Detection/Tracking](https://mlsanity.com/paper/1811.05340/) (2018)
- [Scheduling Real-time Deep Learning Services as Imprecise Computations](https://mlsanity.com/paper/2011.01112/) (2020)
- [Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking](https://mlsanity.com/paper/1708.00153/) (2017)
- [On the Optimal Solutions of the Infinite-Horizon Linear Sensor Scheduling Problem](https://mlsanity.com/paper/1312.0157/) (2013)
- [Latency-Reliability Tradeoffs for State Estimation](https://mlsanity.com/paper/1810.11831/) (2018)
- [Consistent Discretization of a Class of Predefined-Time Stable Systems](https://mlsanity.com/paper/2001.08318/) (2020)

## Abstract (arXiv)

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.