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

This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria. Pollen images of 16 types: <code>images_16_types.zip</code> 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 <code>pollen_video_library.zip</code> 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]. Field data over 3 days are gathered in Graz in spring 2020. <code>pollen_field_data.zip</code> Sample code to load the data and visualize the images is in <code>plot_pollen_sample.py</code>. Download and extract the file <code>images_16_types.zip</code> in the same folder as <code>plot_pollen_sample.py</code> to run the example. <strong>Dependecies</strong> opencv numpy matplotlib <strong>Credit</strong> [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. <pre><code>@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}, } </code></pre>