Dynamic Event-based Optical Identification and Communication
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910436899684352 |
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| author | von Arnim, Axel Lecomte, Jules Borras, Naima Elosegui Wozniak, Stanislaw Pantazi, Angeliki |
| author_facet | von Arnim, Axel Lecomte, Jules Borras, Naima Elosegui Wozniak, Stanislaw Pantazi, Angeliki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_07169 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dynamic Event-based Optical Identification and Communication von Arnim, Axel Lecomte, Jules Borras, Naima Elosegui Wozniak, Stanislaw Pantazi, Angeliki Computer Vision and Pattern Recognition Neural and Evolutionary Computing 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. |
| title | Dynamic Event-based Optical Identification and Communication |
| topic | Computer Vision and Pattern Recognition Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2303.07169 |