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6 results for “Multi-Camera Dataset”
Multi-Camera Action Dataset (MCAD)
<p>Action recognition has received increasing attentions from the computer vision and machine learning community in the last decades. Ever since then, the recognition task has evolved from single view recording under controlled laboratory environment to unconstrained environment (i.e., surveillance environment or user generated videos). Furthermore, recent work focused on other aspect of action recognition problem, such as cross-view classification, cross domain learning, multi-modality learning, and action localization. Despite the large variations of studies, we observed limited works that explore the open-set and open-view classification problem, which is a genuine inherited properties in action recognition problem. In other words, a well designed algorithm should robustly identify an unfamiliar action as “unknown” and achieved similar performance across sensors with similar field of view. The Multi-Camera Action Dataset (MCAD) is designed to evaluate the open-view classification problem under surveillance environment.</p> <p>In our multi-camera action dataset, different from common action datasets we use a total of five cameras, which can be divided into two types of cameras (StaticandPTZ), to record actions. Particularly, there are three Static cameras (Cam04 & Cam05 & Cam06) with fish eye effect and two PanTilt-Zoom (PTZ) cameras (PTZ04 & PTZ06). Static camera has a resolution of 1280×960 pixels, while PTZ camera has a resolution of 704×576 pixels and a smaller field of view than Static camera. What’s more, we don’t control the illumination environment. We even set two contrasting conditions (Daytime and Nighttime environment) which makes our dataset more challenge than many controlled datasets with strongly controlled illumination environment.The distribution of the cameras is shown in the picture on the right.</p> <p>We identified 18 units single person daily actions with/without object which are inherited from the KTH, IXMAS, and TRECIVD datasets etc. The list and the definition of actions are shown in the table. These actions can also be divided into 4 types actions. Micro action without object (action ID of 01, 02 ,05) and with object (action ID of 10, 11, 12 ,13). Intense action with object (action ID of 03, 04 ,06, 07, 08, 09) and with object (action ID of 14, 15, 16, 17, 18). We recruited a total of 20 human subjects. Each candidate repeats 8 times (4 times during the day and 4 times in the evening) of each action under one camera. In the recording process, we use five cameras to record each action sample separately. During recording stage we just tell candidates the action name then they could perform the action freely with their own habit, only if they do the action in the field of view of the current camera. This can make our dataset much closer to reality. As a results there is high intra action class variation among different action samples as shown in picture of action samples.</p> <p>URL: http://mmas.comp.nus.edu.sg/MCAD/MCAD.html</p> <p><strong>Resources:</strong></p> <ul> <li><strong>IDXXXX.mp4.tar.gz</strong> contains video data for each individual</li> <li><strong>boundingbox.tar.gz</strong> contains person bounding box for all videos</li> <li><strong>protocol.json</strong> contains the evaluation protocol</li> <li><strong>img_list.txt</strong> contains the download URLs for the images version of the video data</li> <li><strong>idt_list.txt</strong> contians the download URLs for the improved Dense Trajectory feature</li> <li><strong>stip_list.txt</strong> contians the download URLs for the STIP feature</li> </ul> <ul> <li>Manual annotated 2D joints for selected camera view and action class (available via http://zju-capg.org/heightmap/)</li> </ul> <p><strong>How to Cite:</strong></p> <p>Please cite the following paper if you use the MCAD dataset in your work (papers, articles, reports, books, software, etc):</p> <ul> <li>Wenhui Liu, Yongkang Wong, An-An Liu, Yang Li, Yu-Ting Su, Mohan Kankanhalli<br> <strong>Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking</strong><br> <em>IEEE Winter Conference on Applications of Computer Vision (WACV)</em>, 2017.<br> http://doi.org/10.1109/WACV.2017.28</li> </ul>
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation
<p>While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. This dataset consists of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object.</p> <p>If you use this dataset in your research, please cite the following publication:</p> <p>T. Grenzdörffer, M. Günther, and J. Hertzberg, “YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation,” in <em>2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31-June 4, 2020</em>. IEEE, 2020.</p> <pre><code>@InProceedings{Grenzdoerffer2020ycbm, title = {{YCB-M}: A Multi-Camera {RGB-D} Dataset for Object Recognition and {6DoF} Pose Estimation}, author = {Grenzd{\"{o}}rffer, Till and G{\"{u}}nther, Martin and Hertzberg, Joachim}, booktitle = {2020 {IEEE} International Conference on Robotics and Automation, {ICRA} 2020, Paris, France, May 31-June 4, 2020}, year = {2020}, publisher = {{IEEE}} }</code></pre> <p>This paper is also available on arXiv: <a href="https://arxiv.org/abs/2004.11657">https://arxiv.org/abs/2004.11657</a></p> <p> </p> <p>To visualize the dataset, follow these instructions (tested on Ubuntu Xenial 16.04):</p> <pre><code class="language-bash"># IMPORTANT: the ROS setup.bash must NOT be sourced, otherwise the following error occurs: # ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type # nvdu requires Python 3.5 or 3.6 sudo add-apt-repository -y ppa:deadsnakes/ppa # to get python3.6 on Ubuntu Xenial sudo apt-get update sudo apt-get install -y python3.6 libsm6 libxext6 libxrender1 python-virtualenv python-pip # create a new virtual environment virtualenv -p python3.6 venv_nvdu cd venv_nvdu/ source bin/activate # clone our fork of NVIDIA's Dataset Utilities that incorporates some essential fixes pip install -e 'git+https://github.com/mintar/Dataset_Utilities.git#egg=nvdu' # download and transform the meshes # (alternatively, unzip the meshes contained in the dataset # to <path to venv_nvdu>/lib/python3.6/site-packages/nvdu/data/ycb/aligned_cm) nvdu_ycb -s # run nvdu_viz to visualize the dataset cd <a subdirectory of the YCB-M dataset with some frames> nvdu_viz --name_filters '*.jpg' </code></pre> <p>For further details, see README.md.</p>
Acoustic video cameras multi-species multi-cameras Training Dataset (TD) for Deep Learning applications
<p>This images dataset, called also TD (Training Dataset), is designed to train/test/validate deep learning models to identify fish species in sonar camers video flux. It includes data from two different type of cameras (ARIS and DIDSON), two sites (Touques and Selune rivers in Normandy, France), 6 different fishes classes (Atlantic Salmon, European Eel, Sea Lamprey, Allis Shad, European Catfish and generic unidentified fish). This dataset, formatted in the yolo-darknet format as explained in <a href="https://github.com/AlexeyAB/darknet">https://github.com/AlexeyAB/darknet</a>, includes also a large number of images without any fish passage, in order to test the effect of negative data on the trainings.</p>
Acoustic video cameras multi-species multi-cameras Validation Dataset (VD) for Deep Learning applications
<p>This video dataset, called also VD (Validation Dataset), is designed to test/validate, on a real-world case, deep learning models to identify fish species in sonar camers video flux. It includes data from two different type of cameras (ARIS and DIDSON), two sites (Touques and Selune rivers in Normandy, France), 6 different fishes classes (Atlantic Salmon, European Eel, Sea Lamprey, Allis Shad, European Catfish and generic unidentified fish). This dataset is composed by around 40h of videos, to test the efficiency of the models in the frame of ecological studies and to assess their real-applicability on monitoring sites data. Two sheets are given as the ground truth in which all fish passages (for fish sizes larger than 20 cm) are annotated. No bounding boxes are given.</p>
Non-Overlapping Multi-Camera Calibration Dataset using Icosahedron and Cube Calibration Object
<p>Our dataset comprises images captured using two 3D calibration objects: an icosahedron and a cube. Six 20MP monochrome cameras were arranged in a semicircular configuration with minimal overlapping fields of view. A consistent robotic motion pattern was employed to acquire sequential images for both calibration objects.</p> <p>This work is conducted at the Institute for Factory Automation and Production Systems(<a href="https://www.faps.fau.eu/">FAPS</a>), University of Erlangen-Nuremberg, Germany.</p>
TULIP Dataset (CVPR 2024): Multi-Camera Videos and Clinician Ratings of the MDS-UPDRS Part III Motor Exam for Parkinson's Disease Assessment
<div> <p><strong>TULIP Dataset (Version 1.0.3)</strong></p> </div> <div> <p>** Updates! We include one more subject, so total we have 12 subjects in this dataset.</p> </div> <div> <p><strong>Overview:</strong><br>The TULIP (Three-dimensional Understanding and Learning of Impairments in Parkinson’s) dataset provides high-resolution RGB data from multi-camera setups, supporting research on precision motor assessments for Parkinson’s Disease (PD). Version 1.0.0 features synchronized RGB data from six cameras, capturing multiple angles of PD and healthy participants performing clinically relevant motor tasks. We chose the name TULIP, a nod to the floral emblem of PD research and advocacy, to symbolize our goal for this dataset, to foster transformative new machine learning approaches for PD understanding and treatment.</p> </div> <div>This dataset was published as part of our <a title="https://openaccess.thecvf.com/content/CVPR2024/html/Kim_TULIP_Multi-camera_3D_Precision_Assessment_of_Parkinsons_Disease_CVPR_2024_paper.html" href="https://openaccess.thecvf.com/content/CVPR2024/html/Kim_TULIP_Multi-camera_3D_Precision_Assessment_of_Parkinsons_Disease_CVPR_2024_paper.html" target="_blank" rel="noopener noreferrer">CVPR 2024 Paper.</a></div> <div> </div> <div>Kyungdo Kim, Sihan Lyu, Sneha Mantri, Timothy W. Dunn; <em>TULIP: Multi-camera 3D Precision Assessment of Parkinson's Disease</em>; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 22551-22562</div> <p> </p> <p><strong>Data Contents:</strong><br>RGB videos are organized by subject ID and activity, with synchronized recordings from six camera perspectives per activity. Each video documents motor tasks such as gait and finger tapping, in line with the UPDRS standards for PD, allowing for a detailed study of joint angles, tremors, and other movements key to understanding PD progression.</p> <p>· <em><strong> </strong>Multi-Camera Video Data:</em> RGB videos from six cameras enable robust 3D pose extraction.</p> <p>· <em>Metadata: </em>Includes camera parameters (intrinsic and extrinsic matrices for 3D reconstruction) and task descriptions.</p> <div> <p>· <em>Clinical Examination Labels:</em> Task-specific labels aligned with clinical motor assessments, with annotations from three clinicians to aid in automated scoring models. We also provided the labels in the csv file format.</p> </div> <div> <p> </p> </div> <p><strong>File Structure:</strong><br>Data is organized by Subject ID > Activities > Camera Perspective, with each folder containing RGB video files. Annotations and activity labels are available in a CSV file for easy correlation of tasks with motor patterns. For the camera parameters (pickle file), each subject has its own set of parameters. When you open the pickle file, the order of the elements is as follows: [proj_matrices, cam_matrices, extrinsic_matrices, rmatrices, rvecs, tvecs, distcoeffs]. Here’s a brief description of each:</p> <p>· <em>proj_matrices: </em>Camera projection matrix (3x4 format)</p> <p>· <em>cam_matrices:</em> Intrinsic camera matrix</p> <p>· <em>extrinsic_matrices:</em> Extrinsic camera matrix</p> <p>· <em>rmatrices:</em> Rotation matrix</p> <p>· <em>rvecs:</em> Rotation vector</p> <p>· <em>tvecs:</em> Translation vector</p> <div> <p>· <em>distcoeffs:</em> Distortion coefficients, which is a zero matrix in our case.</p> </div> <div> <p> </p> </div> <p><strong>Privacy and Consent:</strong><br>Faces are blurred to ensure privacy. This initial version of the dataset contains data from 12 participants, with data from the remaining 3 participants expected to be released in the near future.</p> <p><strong>Code Availability:</strong><br>Behavioral feature extraction demo code for 3D poses will be available on our github (github link can be found on the <a title="https://www.tulipproject.net/" href="https://www.tulipproject.net/" target="_blank" rel="noopener noreferrer">TULIP Project</a> page). For further details and access to our publication on TULIP data and baseline projects, please visit <a title="https://www.tulipproject.net/" href="https://www.tulipproject.net/" target="_blank" rel="noopener noreferrer">TULIP Project</a> or <a title="https://openaccess.thecvf.com/content/CVPR2024/html/Kim_TULIP_Multi-camera_3D_Precision_Assessment_of_Parkinsons_Disease_CVPR_2024_paper.html" href="https://openaccess.thecvf.com/content/CVPR2024/html/Kim_TULIP_Multi-camera_3D_Precision_Assessment_of_Parkinsons_Disease_CVPR_2024_paper.html" target="_blank" rel="noopener noreferrer">CVPR 2024 Paper</a>.</p> <p><strong>Citing this dataset:</strong></p> <p>Please cite our CVPR paper if use this dataset in your work. Citation:</p> <p>Kyungdo Kim, Sihan Lyu, Sneha Mantri, Timothy W. Dunn; <em>TULIP: Multi-camera 3D Precision Assessment of Parkinson's Disease</em>; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 22551-22562</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.