Conference paper Open Access

Asynchronous event-based clustering and tracking for intrusion monitoring in UAS

Rodríguez-Gómez, J.P.; Gómez Eguíluz, A.; Martínez-de Dios, J.R.; Ollero, Anibal


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/0b6ad370-aa82-4ef8-8748-06f87ac634e7/rodriguez20asynchronous.pdf"
      }, 
      "checksum": "md5:a6633317fe974bb6f1d8c84f382b3dd8", 
      "bucket": "0b6ad370-aa82-4ef8-8748-06f87ac634e7", 
      "key": "rodriguez20asynchronous.pdf", 
      "type": "pdf", 
      "size": 2613614
    }
  ], 
  "owners": [
    64116
  ], 
  "doi": "10.5281/zenodo.3816654", 
  "stats": {
    "version_unique_downloads": 58.0, 
    "unique_views": 75.0, 
    "views": 88.0, 
    "version_views": 88.0, 
    "unique_downloads": 58.0, 
    "version_unique_views": 75.0, 
    "volume": 175112138.0, 
    "version_downloads": 67.0, 
    "downloads": 67.0, 
    "version_volume": 175112138.0
  }, 
  "links": {
    "doi": "https://doi.org/10.5281/zenodo.3816654", 
    "conceptdoi": "https://doi.org/10.5281/zenodo.3816653", 
    "bucket": "https://zenodo.org/api/files/0b6ad370-aa82-4ef8-8748-06f87ac634e7", 
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.3816653.svg", 
    "html": "https://zenodo.org/record/3816654", 
    "latest_html": "https://zenodo.org/record/3816654", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.3816654.svg", 
    "latest": "https://zenodo.org/api/records/3816654"
  }, 
  "conceptdoi": "10.5281/zenodo.3816653", 
  "created": "2020-05-08T07:42:50.274709+00:00", 
  "updated": "2020-07-03T16:02:16.350861+00:00", 
  "conceptrecid": "3816653", 
  "revision": 3, 
  "id": 3816654, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.3816654", 
    "description": "<p>Automatic surveillance and monitoring using Un- manned Aerial Systems (UAS) require the development of per- ception systems that robustly work under different illumination conditions. Event cameras are neuromorphic sensors that cap- ture the illumination changes in the scene with very low latency and high dynamic range. Although recent advances in event- based vision have explored the use of event cameras onboard UAS, most techniques group events in frames and, therefore, do not fully exploit the sequential and asynchronous nature of the event stream. This paper proposes a fully asynchronous scheme for intruder monitoring using UAS. It employs efficient event clustering and feature tracking modules and includes a sampling mechanism to cope with the computational cost of event-by-event processing adapting to on-board hardware computational constraints. The proposed scheme was tested on a real multirotor in challenging scenarios showing significant accuracy and robustness to lighting conditions.</p>", 
    "language": "eng", 
    "title": "Asynchronous event-based clustering and tracking for intrusion monitoring in UAS", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "3816653"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "3816654"
          }
        }
      ]
    }, 
    "grants": [
      {
        "code": "788247", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/501100000780::788247"
        }, 
        "title": "General compliant aerial Robotic manipulation system Integrating Fixed and Flapping wings to INcrease range and safety", 
        "acronym": "GRIFFIN", 
        "program": "H2020", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [], 
          "name": "European Commission", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/501100000780"
          }
        }
      }
    ], 
    "keywords": [
      "event camera", 
      "asynchronous", 
      "intrusion monitoring", 
      "surveillance", 
      "UAS", 
      "clustering", 
      "feature tracking"
    ], 
    "publication_date": "2020-05-08", 
    "creators": [
      {
        "orcid": "0000-0001-7628-1660", 
        "affiliation": "University of Seville", 
        "name": "Rodr\u00edguez-G\u00f3mez, J.P."
      }, 
      {
        "orcid": "0000-0002-2285-2605", 
        "affiliation": "University of Seville", 
        "name": "G\u00f3mez Egu\u00edluz, A."
      }, 
      {
        "orcid": "0000-0001-9431-7831", 
        "affiliation": "University of Seville", 
        "name": "Mart\u00ednez-de Dios, J.R."
      }, 
      {
        "orcid": "0000-0003-2155-2472", 
        "affiliation": "University of Seville", 
        "name": "Ollero, Anibal"
      }
    ], 
    "meeting": {
      "acronym": "ICRA", 
      "url": "https://www.icra2020.org/", 
      "dates": "1-4 June 2020", 
      "place": "Paris, France", 
      "title": "IEEE International Conference on Robotics and Automation"
    }, 
    "access_right": "open", 
    "resource_type": {
      "subtype": "conferencepaper", 
      "type": "publication", 
      "title": "Conference paper"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.3929665", 
        "relation": "isSupplementedBy", 
        "resource_type": "dataset"
      }, 
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.3816653", 
        "relation": "isVersionOf"
      }
    ]
  }
}
88
67
views
downloads
All versions This version
Views 8888
Downloads 6767
Data volume 175.1 MB175.1 MB
Unique views 7575
Unique downloads 5858

Share

Cite as