Dataset Open Access

Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks

Nam Cao; Matthias Meyer; Lothar Thiele; Olga Saukh


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/c407dcf1-61cb-4aed-bfe3-67042f9b830a/Nam_Cao-Dataset_Pollen_Video_Library_for_Benchmarking_Detection_Classification_Tracking_and_Novelty_Detection_Tasks-images_16_types.zip"
      }, 
      "checksum": "md5:654dc11e1c454d7f4bc6e5612ad5ded8", 
      "bucket": "c407dcf1-61cb-4aed-bfe3-67042f9b830a", 
      "key": "Nam_Cao-Dataset_Pollen_Video_Library_for_Benchmarking_Detection_Classification_Tracking_and_Novelty_Detection_Tasks-images_16_types.zip", 
      "type": "zip", 
      "size": 47749077
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/c407dcf1-61cb-4aed-bfe3-67042f9b830a/Nam_Cao-Dataset_Pollen_Video_Library_for_Benchmarking_Detection_Classification_Tracking_and_Novelty_Detection_Tasks-plot_pollen_sample.py"
      }, 
      "checksum": "md5:53c3d2e94daba598eadf473bc7cca646", 
      "bucket": "c407dcf1-61cb-4aed-bfe3-67042f9b830a", 
      "key": "Nam_Cao-Dataset_Pollen_Video_Library_for_Benchmarking_Detection_Classification_Tracking_and_Novelty_Detection_Tasks-plot_pollen_sample.py", 
      "type": "py", 
      "size": 3486
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/c407dcf1-61cb-4aed-bfe3-67042f9b830a/Nam_Cao-Dataset_Pollen_Video_Library_for_Benchmarking_Detection_Classification_Tracking_and_Novelty_Detection_Tasks-pollen_field_data.zip"
      }, 
      "checksum": "md5:91951f67712dee4bdff23bf5f48eebcc", 
      "bucket": "c407dcf1-61cb-4aed-bfe3-67042f9b830a", 
      "key": "Nam_Cao-Dataset_Pollen_Video_Library_for_Benchmarking_Detection_Classification_Tracking_and_Novelty_Detection_Tasks-pollen_field_data.zip", 
      "type": "zip", 
      "size": 10342411824
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/c407dcf1-61cb-4aed-bfe3-67042f9b830a/Nam_Cao-Dataset_Pollen_Video_Library_for_Benchmarking_Detection_Classification_Tracking_and_Novelty_Detection_Tasks-pollen_video_library.zip"
      }, 
      "checksum": "md5:925b52cd2fb55fe74dfb8c3bd07240a5", 
      "bucket": "c407dcf1-61cb-4aed-bfe3-67042f9b830a", 
      "key": "Nam_Cao-Dataset_Pollen_Video_Library_for_Benchmarking_Detection_Classification_Tracking_and_Novelty_Detection_Tasks-pollen_video_library.zip", 
      "type": "zip", 
      "size": 7938079123
    }
  ], 
  "owners": [
    97762
  ], 
  "doi": "10.5281/zenodo.4120033", 
  "stats": {
    "version_unique_downloads": 31.0, 
    "unique_views": 71.0, 
    "views": 91.0, 
    "version_views": 91.0, 
    "unique_downloads": 31.0, 
    "version_unique_views": 71.0, 
    "volume": 622577537064.0, 
    "version_downloads": 88.0, 
    "downloads": 88.0, 
    "version_volume": 622577537064.0
  }, 
  "links": {
    "doi": "https://doi.org/10.5281/zenodo.4120033", 
    "conceptdoi": "https://doi.org/10.5281/zenodo.4120032", 
    "bucket": "https://zenodo.org/api/files/c407dcf1-61cb-4aed-bfe3-67042f9b830a", 
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.4120032.svg", 
    "html": "https://zenodo.org/record/4120033", 
    "latest_html": "https://zenodo.org/record/4120033", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.4120033.svg", 
    "latest": "https://zenodo.org/api/records/4120033"
  }, 
  "conceptdoi": "10.5281/zenodo.4120032", 
  "created": "2020-10-23T16:23:46.773616+00:00", 
  "updated": "2021-09-13T16:17:19.091989+00:00", 
  "conceptrecid": "4120032", 
  "revision": 7, 
  "id": 4120033, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.4120033", 
    "description": "<p><strong>Dataset description</strong></p>\n\n<p>This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria.</p>\n\n<ul>\n\t<li>\n\t<p>Pollen images of 16 types:&nbsp;<code>...images_16_types.zip</code></p>\n\n\t<ul>\n\t\t<li>Acer Pseudoplatanus</li>\n\t\t<li>Aesculus Carnea</li>\n\t\t<li>Alnus</li>\n\t\t<li>Anthoxanthum</li>\n\t\t<li>Betula Pendula</li>\n\t\t<li>Brassica</li>\n\t\t<li>Carpinus</li>\n\t\t<li>Corylus</li>\n\t\t<li>Dactylis Glomerata</li>\n\t\t<li>Fraxinus</li>\n\t\t<li>Pinus Nigra</li>\n\t\t<li>Platanus</li>\n\t\t<li>Populus Nigra</li>\n\t\t<li>Prunus Avium</li>\n\t\t<li>Sequoiadendron Giganteum</li>\n\t\t<li>Taxus Baccata</li>\n\t</ul>\n\t</li>\n\t<li>\n\t<p>Pollen video library&nbsp;<code>...pollen_video_library.zip</code></p>\n\n\t<ul>\n\t\t<li>Each type of pollen is in a separate folder, there may be multiple videos per type.</li>\n\t\t<li>In each pollen folder, we included images cropped from the videos by YOLO object detection algorithm trained on a subset of pollen images as described in [1].</li>\n\t\t<li>Cropped file name structure&nbsp;<code>[Video file name]_[TrackingID]_[Image index of a grain]_[Frame index in video]</code>\n\t\t<ul>\n\t\t\t<li>Example, if a grain has 5 images, the file name would be:\n\t\t\t<pre><code> Anthoxanthum-grass-20200530-122652_0000000_001_00001.jpg\n Anthoxanthum-grass-20200530-122652_0000000_002_00002.jpg\n ...\n Anthoxanthum-grass-20200530-122652_0000000_005_00005.jpg\n</code></pre>\n\t\t\t</li>\n\t\t</ul>\n\t\t</li>\n\t</ul>\n\t</li>\n\t<li>\n\t<p>Field data over 3 days are gathered in Graz in spring 2020.&nbsp;<code>...pollen_field_data.zip</code></p>\n\t</li>\n\t<li>\n\t<p>Sample code to load the data and visualize the images is in&nbsp;<code>...plot_pollen_sample.py</code>. Download and extract the file&nbsp;<code>...images_16_types.zip</code>&nbsp;in the same folder as&nbsp;<code>...plot_pollen_sample.py</code>&nbsp;to run the example.</p>\n\t</li>\n</ul>\n\n<p><strong>Dependecies:</strong></p>\n\n<ul>\n\t<li>opencv</li>\n\t<li>numpy</li>\n\t<li>matplotlib</li>\n</ul>\n\n<p><strong>Credit</strong></p>\n\n<p>[1] N. Cao, M. Meyer, L. Thiele, and O. Saukh. 2020. Automated Pollen Detection with an Affordable Technology. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN). 108&ndash;119.</p>\n\n<pre><code>@inproceedings{namcao2020pollen,\n  title = {Automated Pollen Detection with an Affordable Technology},\n  author = {Nam Cao and Matthias Meyer and Lothar Thiele and Olga Saukh},\n  booktitle = {Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN)},\n  pages={108\u2013119}\n  month = {2},\t\n  year = {2020},\n}</code></pre>", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "title": "Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks", 
    "notes": "Appears in the Proceedings of the 3rd Workshop on Data Acquisition To Analysis (DATA '20)", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "4120032"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "4120033"
          }
        }
      ]
    }, 
    "communities": [
      {
        "id": "workshopdata"
      }
    ], 
    "publication_date": "2020-10-23", 
    "creators": [
      {
        "orcid": "0000-0003-2058-1335", 
        "affiliation": "Institute of Technical Informatics, Graz University of Technology, Austria", 
        "name": "Nam Cao"
      }, 
      {
        "orcid": "0000-0001-6895-2823", 
        "affiliation": "Computer Engineering and Networks Lab, ETH Zurich", 
        "name": "Matthias Meyer"
      }, 
      {
        "affiliation": "Computer Engineering and Networks Lab, ETH Zurich", 
        "name": "Lothar Thiele"
      }, 
      {
        "orcid": "0000-0001-7849-3368", 
        "affiliation": "Institute of Technical Informatics, Graz University of Technology, Austria, Complexity Science Hub Vienna, Austria", 
        "name": "Olga Saukh"
      }
    ], 
    "access_right": "open", 
    "resource_type": {
      "type": "dataset", 
      "title": "Dataset"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.4120032", 
        "relation": "isVersionOf"
      }
    ]
  }
}
91
88
views
downloads
All versions This version
Views 9191
Downloads 8888
Data volume 622.6 GB622.6 GB
Unique views 7171
Unique downloads 3131

Share

Cite as