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2,610 results for “tracking”
InSAR stack of Western Cape, South Africa from Sentinel-1 ascending track 29 processed with SNAP
<p>A stack of unwrapped interferograms on Western Cape, South Africa.</p> <p>Sensor: Sentinel-1ascending track 29</p> <p>Time: 2019.03.03 - 2019.05.14, 7 acquisitions, 15 interferograms</p> <p>Processor: SNAP (accessed on 14 July 2019)</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p>
Open Satellite Video Single Target Tracking Datasets (OpenSatSTTD)
<p>We collect the latest open-source datasets for satellite video single target tracking (SatSTT) and launch the OpenSatSTTD project to promote the sharing of the latest research datasets in the SatSTT field. Satellite videos in the OpenSatSTTD project are collected from different sensors and platforms, and four targets (i.e., vehicles, trains, airplanes and vessels) are annotated by oriented bounding boxes. Users can obtain all satellite videos in the OpenSatSTTD project from links in the files.</p> <p>Source:</p> <p>Zheng, Ying., Zhu, Q., Luo, J., Li, Z., Lin, Z., Huang, X., and Zhang L.: Single Target Tracking in High-Resolution Satellite Videos: A Comprehensive Review (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6780820, 2022.</p> <p> </p>
Dataset of polygons with the contour of 900 juniper shrubs used to track shrub growth from 1977 to 2020 in Sierra Nevada (Spain) using very high resolution aerial and satellite RGB images.
<p><strong>This database provides as polygons the contours of 900 juniper shrubs (<em>Juniperus communis L.</em> and <em>Juniperus sabina L.</em>) along 5 decades (years 1977, 1984, 2001, 2010 and 2020). The contour of each of 900 shrubs manually mapped using the Google Satellite composite for the year 2020) was tracked back in time using orthophotos provided by REDIAM. Contours were obtained by manual annotation as polygon shapefiles in QGIS 3.10.3. Additionally, for the year 2020, the polygons were characterized with five attributes that gather ecological information: Morphotype (Hemispherical, Striped, Senescent, With rock), Presence of surrounding vegetation (Bare Soil, Surrounding Vegetation), Presence of nearby human land-uses (Surrounded by human facilities within 250 meters, Non-anthropized environment) Health status (as percentage of canopy cover with brown foliage: values between 0-5, where 0 corresponds to 100% photosynthetically active cover, decreasing the photosynthetically active cover until category 5 which corresponds to 100% damaged cover), and the subjective annotation certainty of the GIS technician (values between 0-5, where the value 0 corresponds to a very uncertain annotation up to the value 5 which corresponds to a fairly certain annotation). </strong></p>
GFDL hurricane model track data associated with "Dynamical downscaling projections of late 21st century U.S. landfalling hurricane activity"
<p>These data include North Atlantic tropical cyclone track and intensity for control and projected late 21st century simulation from the GFDL hurricane model used in a <em>Climatic</em> <em>Change</em> manuscript: </p> <p>Knutson, T., J. Sirutis, M. Bender, R. Tuleya, and B. Schenkel, 2022: Dynamical downscaling projections of late 21st century U.S. landfalling hurricane activity. <em>Clim. Change</em>, <strong>171</strong>, 1–23.<br> <br> A readme file included below describes the variables and format of the tropical cyclone track data. Questions about the dataset may be directed to Ben Schenkel (<a href="mailto:benschenkel@gmail.com">benschenkel@gmail.com</a>) and Tom Knutson (<a href="mailto:tom.knutson@noaa.gov">tom.knutson@noaa.gov</a>). </p>
Simulated tracks for the TrackMate-ExTrack module.
<p>Simulated tracks, used in a tutorial on TrackMate-ExTrack, a Java port of the ExTrack software.</p> <p>See this page for more information on ExTrack and the tutorial:</p> <p><a href="https://imagej.net/plugins/trackmate/actions/trackmate-extrack">https://imagej.net/plugins/trackmate/actions/trackmate-extrack </a></p>
ORPHEUS The Movie (MPEG-H audio track)
<p>This is an experimental version of the mid length version of the final ORPHEUS project movie<br> (as published here <a href="https://youtu.be/AW2EB7-zxf4">ORPHEUS-Audio on Youtubel</a>) <br> It has been authored and encoded using MPEG-H audio, thus including these NGA features:</p> <ul> <li>6 presets (perspectives): Main - Underground Narrator - Narrator App EN - Narrator App De - Underground App EN - Underground App DE</li> <li>the narrator level can be adjusted in all presets</li> </ul> <p>Mind: Successful audio playback requires a MPEG-H compatible device!</p>
InSAR stack of San Francisco Bay in California from Sentinel-1 descending track 42 processed with ARIA
<p>A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42</p> <p>Processor: ARIA (processed using ISCE and prepared using <a href="https://github.com/aria-tools/ARIA-tools">ARIA-tools</a> as shown below)</p> <p>Tropospheric delay estimated from ERA5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.2 (~5 GB):</strong><br> Time: 2015.03.01 - 2022.06.04, 189 acquisitions, 961 interferograms<br> Used ARIA-tools and MintPy commands:</p> <pre><code>ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42 ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download prep_aria.py -s ../stack/ -d ../DEM/SRTM_3arcsec.dem -i ../incidenceAngle/*.vrt -a ../azimuthAngle/*.vrt -w ../mask/watermask.msk</code></pre> <p><strong>Version 0.2 (~280 MB; for fast testing of code development)</strong><br> Time: 2016.01.31 - 2017.05.10, 23 acquisitions, 91 interferograms<br> Used ARIA-tools commands (access date Jun 18th, 2022):</p> <pre><code>ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --start 20160101 --end 20170510 ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download</code></pre> <p> </p>
Container spreader pose tracking dataset
<p>This dataset contains image sequences that feature a moving quay crane spreader in a port environment while unloading a container cargo vessel. A container crane spreader is a device that is installed on a crane and used to lift containers after attaching onto them.</p> <p><br> The sequences were acquired from a viewpoint similar to that of the crane operator using a camera installed next to the operator’s cabin at a height of approximately 20 meters above the quay. The camera thus moves with the crane, resulting in a non-stationary image background.</p> <p>The dataset is organized into several RAR archives, one for each sequence. In addition to the undistorted image frames, it includes for every sequence a text file whose each line consists of the frame id for every image, the spreader’s bounding box and the spreader’s 6D pose (Rodrigues vector for the orientation, and the translation vector). The axis-aligned 2D bounding box is in the format <em>x0 y0 w h</em> where <em>(x0, y0)</em> is the top left corner and <em>w x h</em> its size, all in pixels. The spreader’s pose is defined with respect to the camera coordinate frame. Also included are the camera intrinsics matrix K for each sequence along with a common 3D mesh model for the spreader.</p> <p>The spreader’s mesh model is supplied in PLY format. For a certain image frame, a model vertex M transforms to the camera coordinate system as R*M + t, R and t being the spreader’s pose (R is the equivalent rotation matrix). The homogeneous coordinates of that vertex’s projection on the image frame are K*(R*M + t).</p> <p><br> The dataset can support research on topics such as object localization, object detection, pose estimation, tracking, etc.<br> If you use this dataset in your research work, you are kindly asked to cite the following paper in your publications:</p> <p>M. Lourakis and M. Pateraki, "<em>Markerless Visual Tracking of a Container Crane Spreader,</em>" 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021, pp. 2579-2586, doi: <a href="https://doi.org/10.1109/ICCVW54120.2021.00291">10.1109/ICCVW54120.2021.00291</a>.</p>
Data from "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad"
<p>This upload includes data shown in the figures of the Paper "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad".</p>
Multiple Particle Tracking Data from Neonatal Organotypic Rat Brain Slices
<p>The data includes statistical features generated from raw multiple particle tracking data from videos collected during three independent experiments: (1) 5 different brain regions, (2) 3 different treatment conditions in the brain, and (3) 5 different brain ages. </p> <p> </p> <table> <thead> <tr> <th scope="col">Feature</th> <th scope="col">Model Abbreviation</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>alpha</td> <td>alpha</td> <td>Exponent of the anomalous diffusion equation.</td> </tr> <tr> <td>Effective diffusion coefficient</td> <td>D_fit</td> <td>Coefficient of the anomalous diffusion equation</td> </tr> <tr> <td>Kurtosis</td> <td>kurtosis</td> <td>The fourth moment of the projected positions on the dominant eigenvector of the radius gyration tensor (T).</td> </tr> <tr> <td>Asymmetry1</td> <td>asymmetry1</td> <td>Characterizes the asymmetry of the trajectory. Asymmetry1 equals 0 for circularly symmetric trajectories and 1 for linear trajectories.</td> </tr> <tr> <td>Asymmetry2</td> <td>asymmetry2</td> <td>The ratio of the smaller to larger principal radius of gyration.</td> </tr> <tr> <td>Asymmetry3</td> <td>asymmetry3</td> <td>An asymmetry feature that accounts for non-cylindrically symmetric point distributions.</td> </tr> <tr> <td>Aspect ratio</td> <td>AR</td> <td>The ratio of the kong and short side of the trajectory's minimum bounding rectangle. Perfectly symmetric trajectories have an aspect ratio of 1, and aspect ratio increases as trajectories become more elongated. </td> </tr> <tr> <td>Elongation</td> <td>elongation</td> <td>An estimation of amount of extension of the trajectory from its centroid. </td> </tr> <tr> <td>Boundedness</td> <td>boundedness</td> <td>Boundedness quantifies how much a particle with diffusion coefficient <em>D<sub>eff</sub></em> is restricted by a circular confinement of radius <em>r</em> when diffusing for a period of time <span class="math-tex">\(N\Delta t \)</span></td> </tr> <tr> <td>Fractal Dimension</td> <td>fractal_dim</td> <td>Fractal dimension is a measure of how "complicated" a self similar figure is. </td> </tr> <tr> <td>Trappedness</td> <td>trappedness</td> <td>The probability (<span class="math-tex">\(\textit{P}_{\textit{t}} \)</span>) that a particle with duffusion coefficient <em>D<sub>eff</sub></em> is trapped in a region (<em>r<sub>0</sub></em>) for a period of time <span class="math-tex">\(N\Delta t \)</span>. </td> </tr> <tr> <td>Efficiency</td> <td>efficiency</td> <td>The ratio of the squared net displacement to the sum of step lengths. </td> </tr> <tr> <td>Straightness</td> <td>straightness</td> <td>The ratio of the net displacement to the sum of step lengths. </td> </tr> <tr> <td>MSD Ratio</td> <td>MSD_ratio</td> <td>MSD ratio characterizes the shape of the MSD curve. For Brownian motion, it is 0; For restricted motion it is < 0; For directed motion it is > 0. </td> </tr> <tr> <td>Frames</td> <td>frames</td> <td>The total number of frames the trajectory spans. </td> </tr> <tr> <td>Effective Diffusion Coefficient 1</td> <td>Deff1</td> <td>Effective diffusion coefficient at 0.33 s.</td> </tr> <tr> <td>Effective Diffusion Coefficient 2</td> <td>Deff2</td> <td>Effective diffusion coefficient at 3.3s. </td> </tr> </tbody> </table> <p>Mean values were calculated based on surrounding datapoints for alpha, D_fit, kurtosis, asymmetry1, asymmetry2, asymmetry3, AR, elongation, boundedness, fractal_dim, trappedness, efficiency, straightness, MSD_ratio, Deff2, and Deff2. </p> <p> </p>
NLO QCD Track Evolution Kernels for Fourier and Wavelet Methods
<p>Two datasets of LO and NLO evolution numerical kernels for track functions, one for the Fourier series method and the other for the Legendre wavelet method. We also include the corresponding Julia code to numerically solve the track evolution equation based on these kernels. These datasets are used to build docker images for <a href="https://hub.docker.com/r/haochern/qcd-track-evolution-fourier">Fourier</a> and <a href="https://hub.docker.com/r/haochern/qcd-track-evolution-wavelet">wavelet</a> approaches. More details of instructions, as well as the moment method to the track evolution, can be found on <a href="https://github.com/HaoChern14/Track-Evolution">https://github.com/HaoChern14/Track-Evolution</a>. </p> <p> </p>
Tracking Selection using Temporal Population Genomics Data
<p>This repository contains the implementation of a pipeline to run the simulations and to produce a reference table for the ABC-RF inference of demography and selection. In its new release, this repository contains the whole-genome polymorphism of contemporary and museum specimens of <em>Apis mellifera</em> feral populations analyzed by Cridland et al. (2018).</p>
Data: multimodal cell tracking from systemic administration to tumour growth by combining gold nanorods and reporter genes
<p>This data set includes multispectral optoacoustic tomography images supporting an article on cell tracking (preprint: bioRxiv 199836; https://doi.org/10.1101/199836). The corresponding bioluminescence results are included too, as well as the spectra used for the multispectral processing. </p>
InSAR stack of the 2019 Ridgecrest, California earthquake sequence from Sentinel-1 descending track 71 processed with ASF HyP3
<p>A stack of unwrapped interferograms on Owens Valley, California for the <a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">2019 Ridgecrest earthquake sequence</a>.</p> <p>Sensor: Sentinel-1 descending track 71</p> <p>Time: 2019.06.10 - 2019.08.15, 7 acquisitions, 11 interferograms</p> <p>Processor: <a href="https://hyp3-docs.asf.alaska.edu/guides/insar_product_guide/">ASF HyP3</a> (GAMMA)</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy">MintPy</a>.</p>
The bearing capacity of asteroid (65803) Didymos estimated from boulder tracks
<p>This material constitutes the source data and codes used for the the computations and plots of the paper 'The bearing capacity of asteroid (65803) Didymos estimated from boulder tracks' by Bigot, Lombardo et al. This article has been published in Nature Communications on 30 July, 2024.</p> <p>The folder "Source Data" contains an Excel document that provides the raw data used to make the figures and supplementary figures. </p> <p>The folder "Codes" contains the Matlab codes used for the computations of the results, including comments on the figures produced by each code. It also contains a .mat file that consitutes the topographic data of Didymos from Barnouin et al. (2024), used in the code 'TopographyDidymos.m'.</p> <p>The folder "Images" contains the three DART images (DRACO) and the Moon image from LROC used in this article.</p> <p> </p>
Hailstorm Identification and Tracking over Brazil (HIToB): A Storm Polygons Database From GOES ABI Data from 2018 to 2023
<p>This dataset comprises a detailed record of deep convective storm events tracked across South America from 2018 to 2023, utilizing brightness temperature (BT) data from Channel 13 of the GOES-16 Advanced Baseline Imager (ABI) and the TATHU (Tracking and Analysis of Thunderstorms) toolset, that caused hail-fall over Brazil. The database includes storm identification, tracking details, and associated meteorological variables such as brightness temperature statistics inside the storm polygon at each scene and event classifications (e.g., spontaneous generation, continuity, split, merge). The storms were detected and tracked based on brightness temperature threshold of 235 K, with tracking data refined by a 10% overlap criterion between sequential scenes. The tracked convective systems were filtered for intersections in space and time with verified hail reports from Prevots group. The whole family of storm polygons that matched the reports were exported to this database with SpatiaLite enabled dtaa format, in order to make it easier for spatial data queries and analysis. Some example queries using Python library SQLAlchemy are displayed in the code repository as well as the process of creating the tables in the database.<br><br>The data is organized in three tables: "storms", "storm_events" and "intersections". In table "storms" are the records of storm families identifier. Each identifier represents a sequence of storm polygons tracked over subsequent satellite scenes. Table "storm_events" holds the evolution of the storm's geometry through its lifecycle, including BT's mean, minimum and standard deviation inside the storm polygon; as well as storm's pixel count (i.e. storm size). Intersections table stores every instance where a storm event polygon intersects with a hailstorm report's buffer at the corresponding time. In total, there are 9893 intersections belonging to 2172 unique storm families.</p>
The INI-30 Dataset : Event Camera for Eye Tracking
<p>The Ini-30 dataset is collected with two event cameras mounted on a glass frame. Each DVXplorer sensor (640 × 480 pixels) is attached on the side of the frame. The power supply was provided via a 2 meter cable connected from the cameras to a computer, which provided enough freedom of movement. Differently from [2, 24], the participants were not instructed to follow a dot on a screen, but rather encouraged to look around to collect natural eye movements. As shown in Fig. 1, the event cameras were securely screwed on a 3D-printed case attached to the side of the glass frame. The data was annotated based on accumulated linearly decayed events by defining the pixel intensity as function of the linear accumulation of previous pixel intensity. Next we labeled the position of the pupil in the DVS’s array manually, using an assistive labeling tool. We discarded the first 20ms of events to ensure the eye was visible and annotations met the level of image-based annotators. The number of labels per recording was intentionally variable, spanning from 475 to 1’848 with a time per label ranging from 20.0 to 235.77 milliseconds depending on the overall duration of the sample. This setup allows for unconstrained head movements, enables to capture event data from eye movement in a ”in-the-wild” setting and allows the generation of a representative, unique, diverse and challenging dataset.<br><br>NOTE : the annotations relates to the ellipse of the pupil on the image</p>
Drosophila Larvae Tracking: movies of drosophila larvae communities
<h2>33 movies of drosophila larvae communities</h2> <p>The task associated to this dataset is tracking multiple drosophila larvae. Such a tracking is required in the quest to elucidate the genetic basis of Drosophila's behaviour. This dataset was used in the article "<a href="https://hci.iwr.uni-heidelberg.de/sites/default/files/publications/files/219478572/fiaschi_14_tracking.pdf" target="_blank" rel="noopener">Tracking indistinguishable translucent objects over time using weakly supervised structured learning</a>". We provide the raw data, an intermediate segmentation of the foreground and the gold standard used in the <a href="https://hci.iwr.uni-heidelberg.de/sites/default/files/publications/files/219478572/fiaschi_14_tracking.pdf">evaluation of that tracking algorithm</a>. </p>
Earthquake rupture front tracked by polarization azimuths: Codes and extra material
<p>Set of matlab codes to calculate the rupture front position and migration speed every second starting from a set of SAC files.</p> <p>delays_turkey_event.m needs as input SAC files and returns a set of figures displaying the rupture front position and migrations speed. It needs some ad-hoc functions that are contained in this repository. </p> <p>list_of_accelerometers.txt contains the list of instruments processed in the code. </p> <p>turkey_section.py plots the seismic section of a subset of instruments located on or close the East Anatolian Fault line slipped during the Mw 7.8 2023 Kahramanmaraş earthquake.</p> <p>For all details, see Palo and Zollo, Small-scale segmented fault rupture along the East Anatolian Fault during the 2023 Kahramanmaraş earthquake, <em>Commun Earth Environ, 2024. </em>Uploaded files .fig correspond to the source figures of the graphs included in this paper. </p> <p> </p>
Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research
<p><strong>This is the dataset of the report: Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research</strong></p> <p>It contains the following information of all the papers from ASE, FSE, and ICSE in 2023:</p> <ul> <li>Paper title</li> <li>Keyword</li> <li>Is the source data available and accessible in the paper?</li> <li>If the source data is not available, do the authors explain why?</li> <li>Hosting platforms</li> <li>Access mode</li> <li>License</li> <li>Is their experiment data reused from previous work, or newly generated specifically for this study, or combination of both? </li> <li>Do the authors change/modify their experiment data before experiment?</li> <li>What modifications do they perform?</li> <li>Does the link provide detailed instructions about how to replicate their paper?</li> <li>Does the link contains their complete experiment data, their source code or other materials that are necessary to replicate their experiments?</li> <li>What's the data format inside the link?</li> <li>What's the content of the link?</li> </ul> <p> </p> <p>This is a course project and I collect the data in a rush.</p> <p>If you want to use this dataset and find any error, please contact me ;-)</p> <p>My email: echo.xiangchen@gmail.com</p>
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