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19 results for “automated tracking”
Tracking Data I/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p><strong>PLEASE NOTE: the folder compare_postprocessing_synth_bm is provided in the repository 10.5281/zenodo.5227610 due to size restrictions.</strong></p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [<strong>will be stored in 10.5281/zenodo.5227610 due to size restrictions</strong>]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p>
Automated temporal front tracking toolbox in Matlab
<p>This toolbox provides the Matlab scripts that achieve temporal tracking of coherently evolving density fronts in numerical modes. It consists of three components: (1) scripts to detect density fronts based on the Canny edge detection algorithm at each time step in the model outputs; (2) scripts to automatically track coherently evolving front in time; and (3) scripts to do front pruning and remove the incoherent frontal segment that shows inconsistent frontal propagation direction. A dataset ('G_time_rho.mat') containing modeled density at successive time steps is provided for demonstration. More details of this method can be found in our work that is expected to be published soon (Wu, X., F. Feddersen and S. N. Giddings, 2021, Automated temporal tracking of coherently evolving density fronts in numerical models, Journal of Atmospheric and Oceanic Technology, in revision). Contact Xiaodong Wu (x1wu@ucsd.edu) for any questions. </p>
Tracking small animals in complex landscapes: a comparison of localisation workflows for automated radio telemetry systems
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Matching Construction Workers across Views for Automated 3D Vision Tracking On-Site
<p>Computer vision based tracking of construction resources is one of the several options available for obtaining trajectories useful in safety and productivity applications. This type of tracking requires that targets are accurately matched across multiple camera views to obtain a 3D trajectory out of two or more 2D trajectories. This matching is straightforward when it involves easily distinguishable targets in uncluttered scenes. This can be challenging in industrial scenes such as construction sites due to congestion, occlusions and workers in greatly similar high visibility apparel. This paper proposes a novel vision based method that addresses all these issues. It uses as input the output of a 2D vision based tracking method and searches for potential matches in three sequential steps. It terminates only when a positive match is found. The first step returns the strongest candidate by correlating a segment of workers’ past 2D trajectories. The second employs geometric restrictions, whilst the third correlates colour intensity values. The proposed method featured a promising performance of 97% precision, 98% recall and 95% accuracy.</p>
Dataset for: ASTA (Automated Satellite Tracking for Astronomy)
<p>This is the dataset used in the paper Automated Detection of Satellite Trails in Ground-Based Observations Using U-Net and Hough Transform for Training, Test and Validation sets. </p> <p>Additionally, it contains the data table with both matched and unmatched trails detected and discussed in the paper's Application section.<br><br>If you use the dataset, please cite the appropriate DOIs.</p>
Workflow Automation of SEM Acquisitions and Feature Tracking
<p>Data related to the paper <em>Workflow Automation of SEM Acquisitions and Feature Tracking. </em>Includes images used for nanoparticle analysis and for tracking.</p>
Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><strong>PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm </strong></p> <p><strong>All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions.</strong></p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p> </p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p> <p> </p> <p> </p>
Cell tracking data from: Automated timelapse data segmentation reveals in vivo cell state dynamics
<p>Embryonic development proceeds as a series of orderly cell state transitions built upon noisy molecular processes. Here, we defined gene expression and cell motion states using single cell RNA sequencing data and in vivo timelapse cell tracking data of the zebrafish tailbud. We performed a parallel identification of these states using dimensional reduction methods and a change point detection algorithm. Both types of cell states were quantitatively mapped onto embryos, and we utilized the cell motion states to study the dynamics of biological state transitions over time. The time average pattern of cell motion states is reproducible among embryos. However, individual embryos exhibit transient deviations from the time average forming left-right asymmetries in collective cell motion. Thus, the reproducible pattern of cell states and bilateral symmetry arises from temporal averaging. In addition, collective cell behavior can be a source of asymmetry rather than a buffer against noisy individual cell behavior.</p>
Online appendix for "Leveraging Execution Trace with ChatGPT: A Case Study on Automated Fault Diagnosis" (New Ideas and Emerging Results Track in ICSME 2023)
<p>All the prompts we prepared for ChatGPT and the fault diagnosis results</p> <ul> <li>prompt_*: Prompt for ChatGPT <ul> <li>prompt_ChatGPT_setup_*.txt: Prompts to setup ChatGPT before starting the question to ChatGPT</li> <li>The other prompts: Prompts input to ChatGPT for fault diagnosis</li> </ul> </li> <li>result_*: Response from ChatGPT</li> <li>without_trace: Prompt or result when execution trace is not entered in ChatGPT</li> <li>with_trace: Prompt or result when execution trace is entered in ChatGPT</li> </ul>
Experimental Validation of Automated OMA and Mode Tracking for Structural Health Monitoring of Transmission Towers
<p>This dataset is part of the Smart Tower Project and is intended to accompany academic publications entitled "Experimental Validation of Automated OMA and Mode Tracking for Structural Health Monitoring of Transmission Towers". It provides valuable data under high wind excitation conditions and is beneficial for researchers in the field of structural dynamics and Structural Health Monitoring (SHM). The dataset includes high-resolution accelerometer data and estimated modal parameters, along with part of the code script used for analysis.</p> <p>Dataset Components:</p> <ol> <li> <p><strong>Raw Accelerometer Data (April Month)</strong>: High-resolution time-series accelerometer data recorded during April, under high wind excitation conditions. The data is sampled at a rate of 250 Hz. The data is organized in dd/YYYYmmdd_HHMMSS.tdms</p> <ul> <li><strong>Format</strong>: TDMS</li> </ul> </li> <li> <p><strong>Modal Parameters</strong>: Resonance frequencies estimated over the entire monitoring period from 29/03/2023 to 25/06/2023.</p> <ul> <li><strong>Format</strong>: Parquet</li> </ul> </li> </ol>
Cell tracking data from: Automated timelapse data segmentation reveals in vivo cell state dynamics
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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>
User-centric Study of Patients' Receptiveness Towards the Web-based Automated Vision Impairment Gaze-tracking Analysis Systems
ClinicalTrials.gov study NCT07338513. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Data from: The role of social and ecological processes in structuring animal populations: a case study from automated tracking of wild birds
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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>
Data from: Automated single particle detection and tracking for large microscopy datasets
Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates.
Data from: Automated single particle detection and tracking for large microscopy datasets
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Prototyping a Device for Automating Stool and Urine Output Tracking
ClinicalTrials.gov study NCT05438199. IPD Sharing: NO. Countries: 1. Publications: 0.
Automated Discovery of Flight Track Anomalies
As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high-volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.
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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.