Dataset Open Access

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

Nam Cao; Matthias Meyer; Lothar Thiele; Olga Saukh


Dublin Core Export

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Nam Cao</dc:creator>
  <dc:creator>Matthias Meyer</dc:creator>
  <dc:creator>Lothar Thiele</dc:creator>
  <dc:creator>Olga Saukh</dc:creator>
  <dc:date>2020-10-23</dc:date>
  <dc:description>Dataset description

This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria.


	
	Pollen images of 16 types: ...images_16_types.zip

	
		Acer Pseudoplatanus
		Aesculus Carnea
		Alnus
		Anthoxanthum
		Betula Pendula
		Brassica
		Carpinus
		Corylus
		Dactylis Glomerata
		Fraxinus
		Pinus Nigra
		Platanus
		Populus Nigra
		Prunus Avium
		Sequoiadendron Giganteum
		Taxus Baccata
	
	
	
	Pollen video library ...pollen_video_library.zip

	
		Each type of pollen is in a separate folder, there may be multiple videos per type.
		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].
		Cropped file name structure [Video file name]_[TrackingID]_[Image index of a grain]_[Frame index in video]
		
			Example, if a grain has 5 images, the file name would be:
			 Anthoxanthum-grass-20200530-122652_0000000_001_00001.jpg
 Anthoxanthum-grass-20200530-122652_0000000_002_00002.jpg
 ...
 Anthoxanthum-grass-20200530-122652_0000000_005_00005.jpg

			
		
		
	
	
	
	Field data over 3 days are gathered in Graz in spring 2020. ...pollen_field_data.zip
	
	
	Sample code to load the data and visualize the images is in ...plot_pollen_sample.py. Download and extract the file ...images_16_types.zip in the same folder as ...plot_pollen_sample.py to run the example.
	


Dependecies:


	opencv
	numpy
	matplotlib


Credit

[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). 108–119.

@inproceedings{namcao2020pollen,
  title = {Automated Pollen Detection with an Affordable Technology},
  author = {Nam Cao and Matthias Meyer and Lothar Thiele and Olga Saukh},
  booktitle = {Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN)},
  pages={108–119}
  month = {2},	
  year = {2020},
}</dc:description>
  <dc:description>Appears in the Proceedings of the 3rd Workshop on Data Acquisition To Analysis (DATA '20)</dc:description>
  <dc:identifier>https://zenodo.org/record/4120033</dc:identifier>
  <dc:identifier>10.5281/zenodo.4120033</dc:identifier>
  <dc:identifier>oai:zenodo.org:4120033</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.4120032</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/workshopdata</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
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