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6 results for “Crowd Detection”
Datasets for Crowd-based Requirements Engineering and aspect-based detection of learning-centered emotion from the text in Serbian language
<h1>Datasets for the paper "Enhancing Software and Learning with Serbian Student Feedback Corpora"</h1> <p>These datasets include student feedback on an Intelligent Tutoring System written in Serbian, annotated with categories for Crowd-based Requirements Engineering (CrowdRE) and aspect-based detection of learning-centered emotions. Four annotators manually annotated each sentence. </p> <p>The CrowdRE dataset includes two JSON files:</p> <ul> <li><strong>crowdre_english.json</strong> - annotated text with columns and classes written in English. Columns are: <ul> <li><em>Comment</em> - the entire student feedback</li> <li><em>Sentence</em> - sentence extracted from the feedback that was annotated</li> <li><em>Intention </em>- class representing the intention of the sentence</li> <li><em>Topic </em>- class representing the topic of the sentence</li> </ul> </li> <li><strong>crowdre_srpski.json</strong> - annotated text with columns and classes written in Serbian. Columns are:<br> <ul> <li><em>Komentar</em> - the entire student feedback</li> <li><em>Recenica </em>- sentence extracted from the entire feedback that was annotated</li> <li><em>Namera </em>- class representing the intention of the sentence</li> <li><em>Tema </em>- class representing the topic of the sentence.</li> </ul> </li> </ul> <p>The dataset for the aspect-based detection of learning-centered emotions includes two JSON files:</p> <ul> <li><strong>emotions_english.json </strong>- annotated text with columns and classes written in English. Columns are: <ul> <li><em>Comment </em>- the entire student feedback</li> <li><em>Sentence </em>- sentence extracted from the feedback that was annotated</li> <li><em>Aspect </em>- class representing the aspect of the sentence</li> <li><em>Emotion </em>- class representing the learning-centered emotion of the sentence</li> </ul> </li> <li><strong>emocije_srpski.json </strong>- annotated text with columns and classes written in Serbian. Columns are: <ul> <li><em>Komentar </em>- the entire student feedback</li> <li><em>Recenica</em> - sentence extracted from the entire feedback that was annotated</li> <li><em>Aspekt</em> - class representing the aspect of the sentence</li> <li><em>Emotion </em>- class representing the learning-centered emotion of the sentence.</li> </ul> </li> </ul> <p>Annotators annotated the dataset based on the annotation procedure and guidelines available <a href="https://github.com/Clean-CaDET/student-feedback-mining">here</a>. </p> <h2>Citation</h2> <p>If you use this in your research, please cite:</p> <blockquote> <p>Vidaković, D., Luburić, N., Kovačević, A., & Slivka, J. Enhancing software and learning with Serbian student feedback corpora. Language Resources & Evaluation (2025). https://doi.org/10.1007/s10579-025-09855-y</p> </blockquote> <p> </p>
Crowd simulation (CrowdSim2) for tracking and object detection
<p>CrowdSim2 is an extension of crowd simulation tool designed in Unity for the purpose of generation massive synthetic data. Such a generated data from crowd simulation enables to validate various methods in terms of tracking multiple people and detect objects (in that example pedestrians and cars). </p> <table align="center"> <caption>Information summarizing number of folders, seconds and frames of data for different weather conditions</caption> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Folders</th> <th scope="col">Seconds</th> <th scope="col">Frames</th> </tr> </thead> <tbody> <tr> <td>Sun</td> <td>2899</td> <td>86 970</td> <td>2 174 250</td> </tr> <tr> <td>Rain</td> <td>1633</td> <td>48 990</td> <td>1 224 750</td> </tr> <tr> <td>Fog</td> <td>1653</td> <td>49 590</td> <td>1 239 750</td> </tr> <tr> <td>Snow</td> <td>1646</td> <td>49 380</td> <td>1 234 500</td> </tr> </tbody> </table> <p>Due to the limitations of the Zenodo platform, we could only include part of data here. If you are interested in the entire collection - please visit the project website: <a href="https://crowdsim.aei.polsl.pl/">CrowdSim</a></p> <p><strong>Acknowledgments</strong> </p> <p>This work was supported by: European Union funds awarded to Blees Sp. z o.o. under grant POIR.01.01.01-00-0952/20-00 “Development of a system for analysing vision data captured by public transport vehicles interior monitoring, aimed at detecting undesirable situations/behaviours and passenger counting (including their classification by age group) and the objects they carry”); EC H2020 project “AI4media: a Centre of Excellence delivering next generation AI Research and Training at the service of Media, Society and Democracy” under GA 951911; research project (RAU-6, 2020) and projects for young scientists of the Silesian University of Technology (Gliwice, Poland); research project INAROS (INtelligenza ARtificiale per il mOnitoraggio e Supporto agli anziani), Tuscany POR FSE CUP B53D21008060008. Publication supported under the Excellence Initiative - Research University program implemented at the Silesian University of Technology, year 2022. This research was supported by the European Union from the European Social Fund in the framework of the project ”Silesian University of Technology as a Center of Modern Education based on research and innovation” POWR.03.05.00- 00-Z098/17 We are thankful for students participating in design of Crowd Simulator: Piotr Bartosz, Stanisław Wróbel, Marcin Wola, Angelika Gluch and Marek Matuszczyk. </p> <p> </p> <p><strong>Citing the Crowdsim 2</strong></p> <p>The Crowdsim 2 is released under a Creative Commons Attribution license, so please cite the Crowdsim 2 if it is used in your work in any form.<br> Published academic papers should use the academic paper citation for our Crowdsim 2 paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people</p> <pre><code class="language-markdown">TBA: Article citations using this dataset will appear here</code></pre> <p>and this Zenodo Dataset</p> <pre><code class="language-markdown">@dataset{crowdsim2_zenodo, title={Crowd simulation (CrowdSim2) for tracking and object detection}, DOI={10.5281/zenodo.7262220}, publisher={Zenodo}, author={Agnieszka Szczęsna and Paweł Foszner and Adam Cygan and Bartosz Bizoń and Michał Cogiel and Dominik Golba and Luca Ciampi and Nicola Messina and Elżbieta Macioszek and Michał Staniszewski}, year={2023}, month={Feb} }</code></pre> <p> </p>
Rethinking pose estimation in crowds: overcoming the detection information bottleneck and ambiguity
<p>#############</p><p>Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity, ICCV 2023</p><p>#############</p><p> </p><p>Authors: Zhou, Mu and Stoffl, Lucas and Mathis, Mackenzie Weygandt and Mathis, Alexander</p><p>Affiliation: EPFL</p><p>Date: October, 2023</p><p> </p><p>Here we provide neural networks weights for the best models in our article "Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity", ICCV 2023. Each model has the naming convention "dataset"-"modeltype".pth</p><p>These pth files can be loaded with PyTorch. The code to load and use the models is available at The code to load and use the models is available at: <a href="https://github.com/amathislab/BUCTD">https://github.com/amathislab/BUCTD</a></p><p><strong>Note: The weights for OCHuman</strong>, are called COCO-* as one only trains on COCO. So OCHuman-X := COCO-X</p><p>We also share the predictions from various bottom-up models to reproduce the training stored in *.json format (compressed as zip files). See our repository for more details.</p><p> </p><p>Link to the ICCV article: </p><p><a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Zhou_Rethinking_Pose_Estimation_in_Crowds_Overcoming_the_Detection_Information_Bottleneck_ICCV_2023_paper.pdf">https://openaccess.thecvf.com/content/ICCV2023/papers/Zhou_Rethinking_Pose_Estimation_in_Crowds_Overcoming_the_Detection_Information_Bottleneck_ICCV_2023_paper.pdf</a></p><p> </p><p>The weights and predictions are released with Creative Commons Attribution 4.0 license. The code is released under the Apache 2.0 license, see https://github.com/amathislab/BUCTD </p><p><i>If you find our weights, code or ideas useful, please cite:</i></p><p> </p><p>@InProceedings{Zhou_2023_ICCV,</p><p> author = {Zhou, Mu and Stoffl, Lucas and Mathis, Mackenzie Weygandt and Mathis, Alexander},</p><p> title = {Rethinking Pose Estimation in Crowds: Overcoming the Detection Information Bottleneck and Ambiguity},</p><p> booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},</p><p> month = {October},</p><p> year = {2023},</p><p> pages = {14689-14699}</p><p>}</p><p> </p>
New Visual Acuity and Crowding Tests for Better Detection of Amblyopia
ClinicalTrials.gov study NCT03505606. IPD Sharing: NO. Countries: 1. Publications: 12.
Crowd Detection Dataset
<h1>Growd Detection Dataset</h1> <p>This dataset comprises aerial images captured from varying altitudes using UAVs. The dataset includes annotations in the form of both bounding boxes and point annotations, totaling <strong>84,486</strong> annotations specifically identifying human heads.</p> <h2><br>Dataset Details</h2> <ul> <li>AgiosGeorgios_27.5 & Civil Defense <ul> <li>Photos of civil defense individuals (crowd of ~10 & ~20) from high and medimum altitudes, both far and near the sea shore </li> </ul> </li> </ul> <p> </p> <ul> <li>Kornos_labeling <ul> <li>\DJI_0042 <ul> <li>Photos of individuals (crowd of ~15) from high and medimum altitudes</li> </ul> </li> <li>\DJI_0049 <ul> <li>Photos of individuals (crowd of ~30) far away and near</li> </ul> </li> <li>\DJI_0050 <ul> <li>Photos of individuals (crowd of ~30) from high and low altitutes</li> </ul> </li> <li>\DJI_0085 <ul> <li>Photos of individuals (crowd of ~30) from far away and near</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>UCY Campus - Low altitude <ul> <li>\data <ul> <li>Photos of individuals (crowd of ~10) from low altitutes at the UCY campus</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>UCY Campus <ul> <li>\part1 <ul> <li>Photos of individuals from high altitude in campus, mostly near of parking area at UCY campus</li> </ul> </li> <li>\part2 <ul> <li>Photos of individuals from high altitude in campus, in a parking area at UCY campus</li> </ul> </li> <li>\part3 <ul> <li>Photos of individuals from high altitude in campus, in a parking area at UCY campus</li> </ul> </li> <li>\part4 <ul> <li>Photos of individuals from high altitude in campus, mostly near of parking area at UCY campus</li> </ul> </li> <li>\part5 <ul> <li>Photos of individuals from high altitude in campus, mostly near of parking area at UCY campus</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>UCY_2023 <ul> <li>\part1 <ul> <li>Photos of individuals from high altitude in campus, mostly near of parking area at UCY campus</li> </ul> </li> <li>\part2 <ul> <li>Photos of individuals from high altitude in campus, mostly near of parking area at UCY campus (not annotated, can be used for testing)</li> </ul> </li> <li>\part3 <ul> <li>Photos of individuals from high altitude in campus, mostly near of parking area at UCY campus (not annotated, can be used for testing)</li> </ul> </li> </ul> </li> </ul>
Correction to "Detecting distortions of peripherally presented letter stimuli under crowded conditions"
<p>We discovered a bug in the code producing radial frequency distortions for our paper</p> <p>Wallis, T. S. A., Tobias, S., Bethge, M., & Wichmann, F. A. (2017). Detecting distortions of peripherally presented letter stimuli under crowded conditions. Attention, Perception, & Psychophysics. https://doi.org/10.3758/s13414-016-1245-x</p> <p>In this correction, we fixed the bug, remeasured data for Experiment 1 from the paper, and found that our substantive conclusions remain.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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