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15 results for “Video Classification”
Video classification using deep learning
<p>Material accompanying the paper "Frame-by-frame annotation of video recordings using deep neural networks". Contains a selection of videos used in the paper, manual annotations, and results. Code is contained in the associated GitHub repository. See the readme for details.</p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: <a href="https://doi.org/10.5281/zenodo.7924745">https://doi.org/10.5281/zenodo.7924745</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre> <p> </p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
Data set and classification method for low quality web traffic identification in video marketing campaigns
<p>Final outcomes of the InPreVi (AI4Media) project developed in 2022. </p> <p>1. Data set describing the statistics of the video ad marketing campaigns</p> <p>2. Script for web traffic classification</p>
DIVERSE: Deciphering Internet Views on the U.S. Military Through Video Comment Stance Analysis: A Novel Benchmark Dataset for Stance Classification
<p>Paper citation: Cruickshank, Iain J., and Lynnette Hui Xian Ng. "DIVERSE: Deciphering Internet Views on the US Military Through Video Comment Stance Analysis, A Novel Benchmark Dataset for Stance Classification." <em>arXiv preprint arXiv:2403.03334</em> (2024).</p> <p>Link to paper: https://arxiv.org/abs/2403.03334</p>
Supplementary video for "Rapid brain tumor classification from sparse epigenomic data"
<p>Although the intraoperative, molecular differential diagnosis of the approximately one hundred different brain tumor entities described to date has been a goal of neuropathology in the last decade, this has not yet been achieved in a clinically relevant time frame of less than one hour after biopsy collection. Recent advances in third-generation sequencing technologies have brought this once-elusive goal within reach. However, established machine learning techniques rely on concepts and methods, impractical for live diagnostic workflows in clinical applications. Here, we present MethyLYZR, a Naïve Bayesian framework enabling fully tractable live classification of cancer epigenomes. MethyLYZR can be run in parallel with an ongoing Nanopore experiment with negligible computational cost and provides clinically relevant and accurate cancer classification results within 15 minutes of sequencing. Therefore, only the time required for DNA extraction and the Nanopore sequencer's maximum parallel throughput remain limiting factors for even faster time-to-results. We demonstrate the potential utility of the MethyLYZR framework not only for the neurosurgical intraoperative use case but also for other oncologic indications and cell-free DNA from liquid biopsies.</p> <div> <div> <div> <p> </p> <p>The video is a composite of 10 clinical demonstrator runs with timings for DNA extraction and library preparation.</p> </div> </div> </div>
Video recording and vegetation classification elucidate sheep foraging ecology in species-rich grassland
<p>Dataset as Excel file</p>
Video Classification of Intubation in Spain (VCI)
ClinicalTrials.gov study NCT06537531. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Using artificial intelligence classification of videos to examine the environmental, evolutionary and physiological constraints on provisioning behavior
<p>The use of artificial intelligence (AI) technologies can revolutionize how we approach data collection and analysis in behavioral ecology. One such example is in provisioning behavior. Parents of altricial species are selected to provide parental care (such as food provisioning) for their offspring, but there is substantial variation in the level of this care. Provisioning rate may be determined environmentally, by the physiological ability of parents and needs of nestlings, or by evolutionary incentives. We quantified provisioning rate in 20 purple martin (<i>Progne subis</i>) nests in the context of an experimental reduction of nest ectoparasites. 10 nests had a parasite reduction treatment, and 10 nests were controls. By using AI to automate the analysis of nest camera videos we were able to obtain nearly-continuous provisioning rate information at a high temporal resolution for the first half of the nestling period. We used random forest modeling to assess the factors determining provisioning rate and found evidence for environmental, evolutionary and physiological constraints and incentives on provisioning. Birds appeared to be environmentally limited in their provisioning in cool, wet conditions, especially later in the breeding season; but adjusted their provisioning according to the changing physiological needs of nestlings. We found evidence for a compensatory response to increased parasite load, in which parents increased provisioning to more heavily parasitized nests.</p>
Data from: Using artificial intelligence classification of videos to examine the environmental, evolutionary and physiological constraints on provisioning behavior
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Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks
<p>This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria.</p> <ul> <li> <p>Pollen images of 16 types: <code>images_16_types.zip</code></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> </li> <li> <p>Pollen video library <code>pollen_video_library.zip</code></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> </ul> </li> <li> <p>Field data over 3 days are gathered in Graz in spring 2020. <code>pollen_field_data.zip</code></p> </li> <li> <p>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.</p> </li> </ul> <p> </p> <p><strong>Dependecies</strong></p> <ul> <li>opencv</li> <li>numpy</li> <li>matplotlib</li> </ul> <p> </p> <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> <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>
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>
Supplementary Video 1 of: Ultra-fast deep-learned CNS tumor classification during surgery
<p><strong>Supplementary Video 1:</strong> An intraoperative sequencing experiment (INTRA_4) was captured on film. Audio has been redacted for privacy reasons. The timeline of this specific experiment is shown as a representative example in Fig. 4.</p>
Video Classification of Intubation in Spain (VCI) (Pediatrics)
ClinicalTrials.gov study NCT06881576. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification
ClinicalTrials.gov study NCT07152093. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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OpenNeuro
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