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10 results for “camera tracking”
Fusion of Underwater Camera and Multibeam Sonar for Diver Detection and Tracking
<div><strong>Context</strong></div> <div> </div> <div>This dataset is related to previously published public dataset "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments". <a href="../records/7728089">https://zenodo.org/records/7728089</a></div> <div>It contains ZED-right camera and sonar images collected from Hemmoor Lake and DFKI Maritime Exploration Hall.</div> <div> </div> <div>Sensors: Low Frq (1.2MHz) Blueprint Oculus M1200d Sonar and ZED Right Camera</div> <div> </div> <div><strong>Content</strong></div> <div> </div> <div>The dataset is created for Diver Detection and Diver Tracking applications.</div> <div> </div> <div>For the Diver Detection part, the dataset is prepared to train, validate and test YOLOv7 model.</div> <div>7095 images are used for training data, and 3095 images are used for validation data. These sets are augmented from originally captured and sampled ZED camera images. Augmentation methods are not applied to the Test data, which contains 822 images. Train and validation contain images from both the DFKI pool and Hemmor Lake, while the test data is only collected from the lake.</div> <div> </div> <div>To distinguish between the original image and the augmented image, check the name coding. </div> <div>Naming of object detection images:</div> <div>original_image_name.jpg</div> <div>if augmented:</div> <div>original_image_name_<augmentation_number_of_the_same_image>.jpg</div> <div> </div> <div>Object Detection Label Format: </div> <div>YOLO [(class), ((x_min + (x_max - x_min)/2) / image_width), ((y_min + (y_max - y_min)/2) / image_height), ((x_max - x_min) / image_width), ((y_max - y_min) / image_height)]</div> <div> </div> <div>Class: "diver", represented by "0" in object detection labels.</div> <div> </div> <div>Resolution of Object Detection Camera Images: 640x640</div> <div>Resolution of Object Tracking Camera Images: 1280x720</div> <div>Resolution of Object Tracking Low Frequency Sonar: 932x514</div> <div> </div> <div>About the Object Tracking on Sonar, the sampled data is the part where diver moves around the table and the platform. </div> <div>There are 4 cases shared in the dataset, which contain a sonar stream, and corresponding ZED-right camera images. </div> <div>Totally, 1193 points represent the diver on sonar images for the diver tracking application.</div> <div> </div> <div>For the tracking, "tracking_sonar_coordinates_<number>.csv" contains x,y coordinates of a point where the diver is in the sonar image. </div> <div>And "image_sonar_<number>.csv" file contains the matching between sonar and camera images.</div> <div> </div> <div><strong>Acknowledgements</strong></div> <div> </div> <div>The data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958.</div> <p> </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>
Toulouse Capitole camera tracking dataset
<p>This dataset collects 56 images taken in Place du Capitole in Toulouse (FRA) (https://goo.gl/maps/rWyj8yCGAMP2) and 3 related videos taken while moving around in the place. The aim of the dataset is to test and evaluate the camera tracking algorithms developed for the POPART project (http://www.popartproject.eu/), and help the reproducibility of the experiments.</p> <p>The camera tracking algorithms developed for the POPART project are based on model tracking of an existing 3D reconstruction of the scene. First a collection of still images are taken and a SfM pipeline is used to perform the 3D reconstruction of the scene. As result of this first step, a 3D point cloud is generated. The camera tracking algorithms are then based on camera localization techniques: each frame is individually localized w.r.t. the 3D point cloud using the photometric information (SIFT features) associated to each point. This allows to align the point cloud to the current frame and thus compute the camera pose.</p> <p>The dataset is maintained at https://gitlab.com/simogasp/trackingDataset_TLSCapitole </p>
Capdigital courtyard camera tracking dataset
<p>This dataset collects 197 images taken in courtyard of Capdigital building (https://goo.gl/maps/1fAw1KqTp9u) in Paris (FRA) and 2 video sequences taken with a camera rig composed of 1 main camera and 2 witness cameras on the side looking outwards. The aim of the dataset is to test and evaluate the camera tracking algorithms developed for the POPART(http://www.popartproject.eu/) project, and help the reproducibility of the experiments. The camera tracking algorithms developed for the POPART project are based on model tracking of an existing 3D reconstruction of the scene. First a collection of still images are taken and a SfM pipeline is used to perform the 3D reconstruction of the scene. As result of this first step, a 3D point cloud is generated.</p> <p>The camera tracking algorithms are then based on camera localization techniques: each frame is individually localized w.r.t. the 3D point cloud using the photometric information (SIFT features) associated to each point. This allows to align the point cloud to the current frame and thus compute the camera pose.</p> <p>The dataset is maintained at https://gitlab.com/simogasp/trackingDataset_CapdigitalOutdoor</p>
Dataset supporting the article "Megapixel camera arrays for high-resolution animal tracking in multiwell plates"
<p>IB and LF contributed equally.</p> <p> </p> <p>This deposition contains the supporting dataset for the article:</p> <p><strong>Megapixel camera arrays for high-resolution animal tracking in multiwell plates</strong></p> <p>Ida Barlow, Luigi Feriani, Eleni Minga, Adam McDermott-Rouse, Thomas J O'Brien, Ziwei Liu, Maximilian Hofbauer, John R Stowers, Erik C Andersen, Siyu S Ding, André EX Brown</p> <p> </p> <p> </p> <p><strong>Acknowledgements</strong>:</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 714853) and was supported by the Medical Research Council through grant MC-A658-5TY30. This work was supported by a Research Grant from HFSP (Ref.-No: RGP0001/2019). AMR was supported by a BBSRC CASE studentship part-funded by Syngenta.</p>
Data from: Seasonality in daily movement patterns of mandrills revealed by combining direct tracking and camera traps
Open the record for dataset details and reuse information.
Visual tracking of animals in three-dimensions using mobile handheld independent GoPro cameras and VSLAM software
Open the record for dataset details and reuse information.
Supplemental Video 1 to Vanzella et al paper: A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents
<p>This video shows how the head tracker described in the manuscript "<strong>A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents" </strong>can track in real time the pose of the head of rat engaged in a perceptual discrimination task.</p>
MC-GTA: A Synthetic Benchmark for Multi-Camera Vehicle Tracking
<p><strong>Dataset</strong></p> <p>The MC-GTA dataset is designed for multi-camera vehicle tracking (MCVT) in urban environments, crucial for city-scale traffic analysis, management, and security applications. Traditional MCVT systems face challenges due to the scarcity of annotated data necessary for training and testing deep learning-based computer vision models. To address this, the MC-GTA dataset offers a synthetic collection of urban scene images captured from the virtual environment of the Grand Theft Auto 5 (GTA) video game. This dataset features recordings from multiple cameras placed at various crossroads, with automatically generated annotations including bounding boxes and unique vehicle IDs consistent across different video sources. The dataset aims to provide a valuable benchmark for MCVT tasks, demonstrating its utility through performance evaluation with a state-of-the-art MCVT approach. Additionally, the dataset and tools for creating custom scenarios are publicly accessible at <a href="https://github.com/GaetanoV10/GT5-Vehicle-BB" target="_new" rel="noreferrer">https://github.com/GaetanoV10/GT5-Vehicle-BB</a>.<br><br></p> <p><strong>Citing the MC-GTA</strong></p> <p>The MC-GTA is released under a Creative Commons Attribution license, so please cite the MC-GTA if it is used in your work in any form.<br>Published academic papers should use the academic paper citation for our MC-GTA paper</p> <blockquote> <pre>@inproceedings{ciampi2023mc, title={Mc-gta: A synthetic benchmark for multi-camera vehicle tracking}, author={Ciampi, Luca and Messina, Nicola and Valenti, Gaetano Emanuele and Amato, Giuseppe and Falchi, Fabrizio and Gennaro, Claudio}, booktitle={International Conference on Image Analysis and Processing}, pages={316--327}, year={2023}, organization={Springer} }</pre> </blockquote> <p>Personal works, such as machine learning projects/blog posts, should provide a URL to the MC-GTA<strong> </strong>Zenodo page (<a href="https://doi.org/10.5281/zenodo.5996890">https://doi.org/10.5281/zenodo.5996890</a>), though a reference to our MC-GTA paper would also be appreciated.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the MC-GTA<strong> </strong>or if you experience any issues downloading files, please contact us at luca.ciampi[at]isti.cnr.it</p> <p><strong>Acknowledgements</strong></p> <p>Supported by: MOST - Sustainable Mobility National Research Center, funded by the European Union Next-GenerationEU (Piano Nazionale di Ripresa E Resilienza (PNRR) - Missione 4 Componente 2, Investimento 1.4 - D.D. 1033 17/06/2022, CN00000023); AI4Media – A European Excellence Centre for Media, Society, and Democracy (EC, H2020 No. 951911); SUN – Social and hUman ceNtered XR (EC, Horizon Europe No. 101092612).</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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.