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24 results for “Object tracking”
Multiple-object tracking as atool for parametrically modulating memory reactivation
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Next-generation 3D object detection and tracking for self-driving vehicles using object velocity
<p>The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the training and testing sets, that include KITTI standard folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:</p><ul><li>Point cloud 1: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_radial_velocity;</li><li>Point cloud 2: (x,y,z,(Float)Absolute_Speed): in this point cloud, every point has the absolute speed of the object as the additional feature. File: velodyne_abs_speed;</li><li>Point cloud 3: (x,y,z,(Bool)Is_Moving): the additional feature of this point cloud is a Boolean value that is set to 1.0 if the object is moving; contrariwise, it is set to 0.0 for static objects. File: velodyne_is_moving;</li><li>Point cloud 4: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_xyz;</li></ul><p>Additionally, the detections generated with the OpenPCDet toolbox and Second-IoU model are provided.</p><p>This work was made as part of a master thesis of Informatics Engineering in the University of Aveiro.</p>
Data associated with Versatile Multiple Object Tracking in Sparse 2D/3D Videos via Deformable Image Registration (2024)
<p>This includes a volumetric whole-brain calcium recording of a freely behaving worm (<em>C. elegans</em>) captured at 4 Hz with tracked fluorescent neuronal nuclei, used to demonstrate the performance of a multi-object tracking algorithm (ZephIR) described in the associated publication.</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>
Object Tracking on a Monopoly Game Board
<p><strong>Object Tracking on a Monopoly Game Board</strong></p> <p><strong>Author:</strong> Nathan Hoebeke</p> <p><strong>Supervisors:</strong> Maxim Van de Wynckel, Prof. Dr. Beat Signer</p> <p><strong>About</strong></p> <p>The goal of this dataset was to track game pieces on the physical game board of Monopoly. We make use of object classification where our training data consists of 100 pictures (taken at an angle) of the game board in order to classify the individual (moving) pieces. The training dataset was on the 9th of April 2023 and the test date recorded on the 7th of May 2023 using an iPhone 13 mini and iPhone 12.</p> <p>Two participants played a game of Monopoly and each individually took pictures of the current game state after every move. These images were then processed by our application to determine the location of pawns and other game pieces such as the red and green houses.</p> <p>Raw images are unprocessed but may have minor edits to ensure anonymisation of participants in the background. We used <a href="https://roboflow.com/">Roboflow</a> to label and train our dataset which is included in this repository.</p> <p>For more information about our processing and this dataset you can download the full Bachelor thesis here: <a href="https://wise.vub.ac.be/thesis/location-tracking-physical-game-board">https://wise.vub.ac.be/thesis/location-tracking-physical-game-board</a> (download link available after embargo at the end of the academic year)</p> <p>This dataset was published as part of the bachelor thesis: <em>Location Tracking on a Physical Game Board</em> for obtaining the degree of Bachelor in Computer Sciences at the <a href="https://vub.be">Vrije Universiteit Brussel</a>.</p> <p><strong>Data</strong></p> <table> <thead> <tr> <th>Data</th> <th>Pictures</th> <th>Device</th> </tr> </thead> <tbody> <tr> <td><strong>Training</strong></td> <td>213</td> <td>iPhone 13 mini</td> </tr> <tr> <td><strong>Test #1</strong></td> <td>102</td> <td>iPhone 12</td> </tr> <tr> <td><strong>Test #2</strong></td> <td>93</td> <td>iPhone 13 mini</td> </tr> </tbody> </table> <p><strong>Dataset contents</strong></p> <ul> <li><code>model</code>: Trained YOLOv5 model with labels. This dataset can also be found <a href="https://universe.roboflow.com/vub-vsmjx/boardgamedetection">here</a>.</li> <li><code>train</code>: Training data made by the author. <ul> <li><code>raw</code>: Raw pictures of the game board at various states. <ul> <li><code>GAME_2023-04-09_<HH>-<MM>-<SS>.jpg</code>: Images formatted based on the date and time when they were captured.</li> </ul> </li> <li><code>processed</code>: Processed pictures with perspective transformation applied. <ul> <li><code>canvas<X>.png</code>: (Pre)-processed image.</li> </ul> </li> </ul> </li> <li><code>test</code>: Test data made by indepedent participants. <ul> <li><code>participant_1</code>: Participant 1 data <ul> <li><code>raw</code>: Raw pictures of the game board taken by the parcipant after every move. <ul> <li><code>GAME_2023-05-07_<HH>-<MM>-<SS>.jpg</code>: Images formatted based on the date and time when they were captured.</li> </ul> </li> <li><code>processed</code>: Processed pictures with perspective transformation applied. Yellow rectangles are included when our own algorithm was able to determine the location. <ul> <li><code>canvas<X>.png</code>: Processed image.</li> </ul> </li> </ul> </li> <li><code>participant_2</code>: Participant 2 data <ul> <li><code>raw</code>: Raw pictures of the game board taken by the parcipant after every move. <ul> <li><code>GAME_2023-05-07_<HH>-<MM>-<SS>.jpg</code>: Images formatted based on the date and time when they were captured.</li> </ul> </li> </ul> </li> </ul> </li> <li><code>README.md</code>: Documentation and information about the dataset.</li> </ul> <p><strong>License</strong></p> <p>This license applies to the dataset for the game Monopoly. Any artwork or intellectual property from the game that is captured by this dataset is property of Hasbro, Inc.</p> <p>Copyright 2022-2023 Nathan Hoebeke, Beat Signer, Maxim Van de Wynckel, Vrije Universiteit Brussel</p> <p>Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the “Dataset”), to deal in the Dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the Dataset is furnished to do so, subject to the following conditions:</p> <p>The above copyright notice and this permission notice shall be included in all copies or substantial portions that make use of the Dataset.</p> <p>THE DATASET IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.</p>
IDInteraction Object Tracking Analysis
<p>Scripts and results for Object Tracking analysis for Automated Behavioural Coding.</p> <p>See https://github.com/IDInteraction/tracking-analysis for more information.</p>
DLOFTBs - Deformable Linear Objects Tracking with B-splines - dataset
<p>A dataset associated with the paper "DLOFTBs - Fast Tracking of Deformable Linear Objects with B-splines".</p> <p>Contains 2D images and ROS bags with 3D videos of deformable linear objects manipulated by humans and robots.</p>
Dataset for benchmarking Multiple Object Tracking and Segmentation (MOTS) in an apple orchard field.
<p>A dataset of temporally consistent apple images and labels taken using UAVs and a wearable sensor in an orchard, consisting of 86000 manually annotated apple instances and 1700 frames annotated in the MOTS (Multi-object Tracking and Segmentation) style.</p> <p>Sequence 0-5 are used for training. Sequence 6-8 are used for testing/validation. Sequence 10-12 are the testing datasets that have "ignore regions" overlays.</p> <p>The code used in the paper can be found on <a href="https://git.wur.nl/said-lab/rt-obj-tracking/">our GitLab.</a></p>
Dataset for Moving Object Tracking based on RCNNshift
<p>The dataset contains 50 videos from OTB2013, annotated by 11 types of image features. The videos are in mp4 format and can be read directly using the VideoCapture function of the Opencv library. The original dataset link is: http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html.</p>
Improving Spine Surgical Care With Real-Time Objective Patient Tracking Using the Apple Watch
ClinicalTrials.gov study NCT04379921. IPD Sharing: NO. Countries: 1. Publications: 15.
MOTS-annotated UAV Vineyard Dataset captured using Multiple Perspectives to avoid Leaf Occlusion for Object Detection and Tracking
<p>This dataset contains UAV RGB videos (MP4) recorded with a Phantom4 RTK in a vineyard during the harvesting campaign of 2023. It also includes frames and annotations (PNG) to boost Object Detection and Tracking of grape bunches. There are two types of videos: (1) videos capturing the side of the canopy from a frontal point of view only, and (2) videos that collect the data from multiple perspectives to avoid leaf occlusion, common in commercial vineyards. All flights were executed 3 meters above ground level, with a clear sky and wind speed below 0.5 m/s. </p>
Objective African Easterly Tracks in Reanalysis Data using "QTrack"
<p>Objective track data for African Easterly Waves in reanalysis data (ERA5 and MERRA2) through 2024. Waves are tracked from June 1st through October 31st of each year. These tracks are produced using <a href="https://github.com/qlawton/QTrack">QTrack</a>, following the method described in <a href="https://doi.org/10.1175/MWR-D-21-0321.1">Lawton et al. (2022)</a>. Note that starting in 2024, tracks including both the ATL and EPAC basins will be included.</p> <p>AEW Track data is updated on a yearly basis, typically in January of the following year. However, because the year 2024 uses preliminary HURDAT data to determine "developing" AEW classification, users are cautioned that these designiations could change pending finialization of the 2024 HURDAT year. </p> <p><strong>Updated with Eastern Pacific Tracks:</strong> New easterly tracks have been produced through 2024 that included systems over the Eastern Pacific basin <em>in addition to </em>those over the Atlantic Ocean. These are stored as "ERA5_WITH_EPAC" and "MERRA2_WITH_EPAC". Please note that while the Atlantic Ocean tracks have been evaluated, the Eastern Pacific tracks have not. So use these data cautiously. </p> <p><strong>If you use this data for your research, please cite:</strong></p> <blockquote> <p>Lawton, Q. A., S. J. Majumdar, K. Dotterer, C. Thorncroft, and C. J. Schreck, 2022: The Influence of Convectively Coupled Kelvin Waves on African Easterly Waves in a Wave-Following Framework. Monthly Weather Review, 150(8), 2055-2072, <a href="https://doi.org/10.1175/MWR-D-21-0321.1" rel="nofollow">https://doi.org/10.1175/MWR-D-21-0321.1</a>.</p> </blockquote> <p>GitHub Repo for QTrack can be found here: <a href="https://github.com/qlawton/QTrack">https://github.com/qlawton/QTrack</a></p> <p>Installable Python Module can be found here: <a href="https://pypi.org/project/qtrack/">https://pypi.org/project/qtrack/</a></p>
I2K 2024 - Object Tracking and Track Analysis using TrackMate and CellTracksColab
<p>Datasets for I2K 2024 - Object Tracking and Track Analysis using TrackMate and CellTracksColab -workshop</p> <p>Course walk through:</p> <p>I2K_2024: <a href="https://github.com/CellMigrationLab/I2K_2024" target="_blank" rel="noopener">https://github.com/CellMigrationLab/I2K_2024</a></p> <p>V4SBD_Winter_school_2025: <a href="https://github.com/CellMigrationLab/V4SDB_Winter_School_2025" target="_blank" rel="noopener">https://github.com/CellMigrationLab/V4SDB_Winter_School_2025</a></p>
MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking - Accompanying Data
<p>Dataset and evaluation data associated to the publication "MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking".</p>
DLOFTBs - Deformable Linear Objects Tracking with B-splines - videos
<p>Videos of the experiments described in the paper "DLOFTBs – Fast Tracking of Deformable Linear Objects with B-splines".</p> <p>Includes 2 videos:</p> <ul> <li>3d_real.mp4 - 3D tracking of real-life DLO</li> <li>3d_art.mp4 - 3D tracking of artificially generated DLO</li> </ul>
Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder
<p>This is the public dataset of synthetic trajectories and MOT17-3T, which is used in paper <a href="https://arxiv.org/abs/2202.09315">Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder</a>. The source code can be found <a href="https://gitlab.inria.fr/xilin/dvae-umot-release">here</a>.</p>
data for preprint: MARGO (Massively Automated Real-time GUI for Object-tracking),a platform for high-throughput ethology by Werkhoven et al
<p>Data for preprint: MARGO (Massively Automated Real-time GUI for Object-tracking),a platform for high-throughput ethology by Werkhoven et al.<br> <br> index.html provides a read-me for the organization of this repository. For its links to work the two .zip files should be extracted.</p>
EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking
<p>The dataset can be used to test your event-based 6-DoF pose tracking algorithm.</p> <p>If you use any of this data, please cite the following publication:</p> <p>@inproceedings{glover2024,<br> title={EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking },<br> author={Glover, Arren and Gava, Luna and Li, Zhichao and Bartolozzi, Chiara},<br> booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},<br> year={2024}<br>}</p> <p>The dataset includes event-driven data and ground truth of 5 objects: dragon, jell-o, mustard, soup can, and spam. For each object, six different motions on independent axes are recorded. </p> <p>To import .log files containing events, we suggest <a href="https://github.com/event-driven-robotics/bimvee">bimvee</a> Python library.</p> <p>Specifically, use the functions to import .log files:</p> <p>data = importIitYarp(filePathOrName=input_path)</p> <p>Ground-truth .csv files have 8 columns, each one corresponding to a different measure: </p> <p>timestamp | x | y | z | qx | qy | qz | qw</p> <p>x, y, z refer to the object position in the camera reference frame, while qx, qy, qz and qw refer to the object orientation expressed in quaternions. </p>
The benchmark datasets for object tracking in satellite videos
<p>A new type of earth observation satellite uses "gaze" method to continuously observe a certain area, and uses "video recording" method to record dynamic information and analyze its instantaneous characteristics. With the development of dynamic acquisition technology for satellite video data, significant progress in target tracking has been made in recent years, which plays an important role in monitoring rapidly changing events. Different from targets in ordinary videos, targets in satellite videos usually demonstrate the phenomenon of a small size occupation pattern (small) and weak feature capture (dim) due to occlusion, illumination variation, and confusion with the surroundings.</p>
The SoccerSum Dataset for Automated Detection, Segmentation, and Tracking of Objects on the Soccer Pitch
<p>The SoccerSum dataset was curated by capturing and annotating soccer videos from the Norwegian Eliteserien league. This collection spans three years, covering 2021, 2022, and 2023. It comprises 750 frames from 41 unique sequences, with 4 to 40 frames per sequence, carefully chosen to represent a diverse selection of scenarios encountered in professional soccer games.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.