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

<p><strong>Dataset description</strong></p><p>This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria.</p><p>Pollen images of 16 types: ...<strong>images_16_types.zip</strong></p><ul><li>Acer Pseudoplatanus</li><li>Aesculus Carnea</li><li>Alnus</li><li>Anthoxanthum</li><li>Betula Pendula</li><li>Brassica</li><li>Carpinus</li><li>Corylus</li><li>Dactylis Glomerata</li><li>Fraxinus</li><li>Pinus Nigra</li><li>Platanus</li><li>Populus Nigra</li><li>Prunus Avium</li><li>Sequoiadendron Giganteum</li><li>Taxus Baccata</li></ul><p>Pollen video library ...<strong>pollen_video_library.zip</strong></p><ul><li>Each type of pollen is in a separate folder, there may be multiple videos per type.</li><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><li>Cropped file name structure [Video file name]_[TrackingID]_[Image index of a grain]_[Frame index in video]<ul><li>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</li></ul></li></ul><p>Field data over 3 days are gathered in Graz in spring 2020. ...<strong>pollen_field_data.zip</strong></p><p> </p><p><strong>Version 2:</strong></p><p>For experiments of mitigating the distribution shift of pollen identification on field data, there are 5 types selected from field data and manually labeled by the expert. The data are zipped in <strong>"the manual_labeled_field_data_5_types.zip"</strong> </p><p>The "<strong>images_5_types_9010_train.zip</strong>" and "<strong>images_5_types_9010_val.zip</strong>" contain 5 types selected from library data (<strong>images_16_types.zip</strong>),  and these correspond to field data. </p><p>The "<strong>images_3_types_for_ablation_study.zip</strong>" contains data on 3 levels of pollen grain hydration. These data are used for the ablation study of model generalization in pollen identification. </p><p>Sample code to load the data and visualize the images is in ...plot_pollen_sample.py. Download and extract the file ...<strong>images_16_types.zip</strong> in the same folder as ...plot_pollen_sample.py to run the example.</p><p><strong>Dependecies:</strong></p><ul><li>opencv</li><li>numpy</li><li>matplotlib</li></ul><p><strong>Credit</strong></p><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). 108–119.</p><p>@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}, }</p><p>Appears in the Proceedings of the 3rd Workshop on Data Acquisition To Analysis (DATA '20)</p&gt