# Dynamic Event-based Optical Identification and Communication

arXiv: https://arxiv.org/abs/2303.07169 · HTML: https://mlsanity.com/paper/2303.07169/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [E-RAFT: Dense Optical Flow from Event Cameras](https://mlsanity.com/paper/2108.10552/) (2021)
- [Real-time clustering and multi-target tracking using event-based sensors](https://mlsanity.com/paper/1807.02851/) (2018)
- [Smart Visual Beacons with Asynchronous Optical Communications using Event Cameras](https://mlsanity.com/paper/2208.01710/) (2022)

## Built on by (most cited first, 2 of 2)

- [Event-based Optical Marker Systems: A survey](https://mlsanity.com/paper/2504.20736/) (2025)
- [Asynchronous Tracking, Optical Communication and 3D Motion Capture using Event-based Sensors](https://mlsanity.com/paper/2610.04342/) (2026)

## Abstract (arXiv)

Optical identification is often done with spatial or temporal visual pattern recognition and localization. Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based cameras and, for tracking, sparse neuromorphic optical flow computed with spiking neurons. The system is embedded in a simulated drone and evaluated in an asset monitoring use case. It is robust to relative movements and enables simultaneous communication with, and tracking of, multiple moving beacons. Finally, in a hardware lab prototype, we demonstrate for the first time beacon tracking performed simultaneously with state-of-the-art frequency communication in the kHz range.