Recently, the coronavirus pandemic has made the use of facial masks and respirators common, the former to reduce the likelihood of spreading saliva droplets and the latter as Personal Protective Equipment (PPE). As a result, this caused problems for the existing face detection algorithms. For this reason, and for the implementation of other more sophisticated systems, able to recognize the type of facial mask or respirator and to react given this information, we created the Facial Masks and Respirators Database (FMR-DB).

Instructions: 

For reasons related to the copyright of the images, we cannot publish the entire database here. If you are a student, a professor, or a researcher and you want to use it for research purposes, send an email to antonio.marceddu@polito.it attaching the license, duly completed, which you can find here on IEEE DataPort.

 

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The AirMuseum dataset is intended for multi-robot stereo-visual and inertial Simultaneous Localization And Mapping (SLAM). It consists in five indoor multi-robot scenarios acquired with ground and aerial robots in a former Air Museum at ONERA Meudon, France. Those scenarios were designed to exhibit some specific opportunities and challenges associated to collaborative SLAM. Each scenario includes synchronized sequences between multiple robots with stereo images and inertial measurements.

Instructions: 

The dataset is organized as follows:

  • sensors.zip holds the calibrations of the cameras and the IMU sensors.
  • apriltags.zip holds the calibration of the mounted apriltag markers for robots B and C. It consists in the estimated pose of the markers' frames with regard to the reference frame attached to one of the robot's cameras.
  • scenarioX_robotY holds the acquisitions of the robotY in scenarioX as ROS .bag files, as well the associated ground-truth trajectories (the ground-truth is provided for the frame attached to cam100 and for the body (inertial) frame).
  • scenarioX_trajectories.mp4 is a video of an accelerated (x20) top-view of the robot trajectories (robotA is red, robotB is green, robotC is blue and the drone is orange)
  • scenarioX_preview.mp4 is a x1.5 accelerated preview of the visual acquisitions of the robots

Additional updated details may be found on the associated github repository (https://github.com/AirMuseumDataset/AirMuseumDataset.git) and in the associated article:  AirMuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial Simultaneous Localization And Mapping - Rodolphe Dubois, Alexandre Eudes and Vincent Frémont - 2020 IEEE International Conference on Multisensor Fusion and Integration (MFI 2020).

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The CHU Surveillance Violence Dataset (CSVD) is a collection of CCTV footage of violent and non-violent actions aiming to characterize the composition of violent actions into more specific actions. We produced several simple action classes for violent and non-violent actions do add variety and better distribution among simple and complex action classes for RGB and Action Silhouette Videos (enhanced Optical Flow Images) with their localized actions.

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This dataset is for date-fruit grading. It contains the grades of three types of dates: Ajwa (grade 1, grade 2, and grade 3), Mabroom (grade 1, grade 2, and grade 3), dried Sukkary (grade 1 and 2)

Instructions: 

This dataset contains images of three types of dates with their grades:

- Ajwah: grade 1, grade 2, and grade 3

- Mabroom: grade 1, grade 2, and grade 3

- Sukkary: grade 1 and grade 2

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The dataset contains 2,400 vehicle images for license plate detection purposes. Images are taken from actively operating commercial cameras which are installed on a highway and in an entrance of a shopping mall. Images

contain generally one vehicle, but sometimes can contain two or more vehicles. For each image in pixel domain there exists two different images generated from encoded High Efficiency Video Coding (HEVC) stream using our method. 

Instructions: 

3 SUB SETS

•2,400 Pixel Domain Images

•2,400 HEVC Domain Images Generated from Our Block Partition Method

•2,400 HEVC Domain Images Generated from Our Prediction Based Method

TRAIN/TEST SETS

•Each train test set contains 1,800 images.

•Each test set contains 600 images.

NAMING CONVENTION

Images are given numeral names starting from 100,001 to 102,400 for each method. The same numbers are used to represent HEVC domain representations of pixel domain images. 

PLATE ANNOTATION

For each image there exists another file which contains plate annotation information in YOLO format.

FOLDER STRUCTURE

+---DB

|   +---HEVCDomain_BlockPartition

|   |   +---Test

|   |   \---Train

|   +---HEVCDomain_PredictionUnit

|   |   +---Test

|   |   \---Train

|   \---PixelDomain

|       +---Test

|       \---Train

 

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We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets c

Instructions: 

* At this moment, the paper of this dataset is under review. The dataset is going to be fully published along with the publication of the paper, while in the meanwhile, more parts of the dataset will be uploaded.

The dataset includes multi-view RGBD, 3D/2D pose, volumetric (mesh/point-cloud/3D character) and audio data along with metadata for spatiotemporal alignment.

The full dataset is splitted per subject and per activity per modality.

There are also two benchmarking subsets, H4D1 for single-person and H4D2 for two-person sequences, respectively.

The fornats are:

  • mRGBD: *.png
  • 3D/2D poses: *.npy
  • volumetric (mesh/point-cloud/): *.ply
  • 3D character: *.fbx
  • metadata: *.txt, *.json

 

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577 Views

Parking Slot Detection dataset

angle, type, and location of each parking slot

Instructions: 

Parking Slot Detection dataset

angle, type, and location of each parking slot

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The data set has been consolidated for the task of Human Posture Recognition. The data set consists of four postures namely -

  1. Sitting,
  2. Standing,
  3. Bending and,
  4. Lying.

There are 1200 images for each of the postures listed above. The images have a dimension of 512 x 512 px.

Instructions: 

The data set has been structured according to the postures. The following directory structure is maintained -

  • Postures.zip
    • Sitting - Contains 1200 images.
    • Standing - Contains 1200 images.
    • Bending - Contains 1200 images.
    • Lying - Contains 1200 images.

To use the data set just unzip the file. The images have been pre-processed in advance. The final images represent relevant silhouettes.

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Images of various foods, taken with different cameras and different lighting conditions. Images can be used to design and test Computer Vision techniques that can recognize foods and estimate their calories and nutrition.

Instructions: 

Please note that in its full view, the human thumb in each image is approximately 5 cm by 1.2 cm.

For more information, please see:

P. Pouladzadeh, A. Yassine, and S. Shirmohammadi, “FooDD: Food Detection Dataset for Calorie Measurement Using Food Images”, in New Trends in Image Analysis and Processing - ICIAP 2015 Workshops, V. Murino, E. Puppo, D. Sona, M. Cristani, and C. Sansone, Lecture Notes in Computer Science, Springer, Volume 9281, 2015, ISBN: 978-3-319-23221-8, pp 441-448. DOI: 10.1007/978-3-319-23222-5_54

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