Omkar2118/datasets huggingface dataset:
OASIS-2
Full name: Open Access Series of Imaging Studies

This set consists of a longitudinal collection of 150 subjects aged 60 to 96. Each subject was scanned on two or more visits, separated by at least one year for a total of 373 imaging sessions. For each subject, 3 or 4 individual T1-weighted MRI scans obtained in single scan sessions are included. The subjects are all right-handed and include both men and women. 72 of the subjects were characterized as nondemented throughout the study. 64 of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with mild to moderate Alzheimer’s disease. Another 14 subjects were characterized as nondemented at the time of their initial visit and were subsequently characterized as demented at a later visit.
NuiSI Dataset
Full name: Nuitrack Skeleton Interaction Dataset

The NuiSI dataset contains skeleton tracking trajectories of Human Interaction Partners performing a variety of physically interactive behaviors (waving, handshaking, rocket fistbump, parachute fistbump) with each other. This is inspired by the dataset in Bütepage et al. "Imitating by generating: Deep generative models for imitation of interactive tasks." Frontiers in Robotics and AI  (2020) wherein they capture a dataset with rokoko motion capture suits. Instead we track the skeletons of the interaction partner with Intel Realsense cameras using Nuitrack, for a more realistic scenario, with noise coming from the depth sensor, the skeleton tracking and some partial occlusions. This makes it more representative of real world interactions with a Robot equipped with an RGBD camera.
T
This dataset is used in our papers for training Interaction models for Human-Robot Interaction with a humanoid social robot. If you find the dataset useful in your work, please cite our paper:

Prasad, V., Heitlinger, L., Koert, D., Stock-Homburg, R., Peters, J., & Chalvatzaki, G. (2023). Learning multimodal latent dynamics for human-robot interaction. arXiv preprint arXiv:2311.16380.

@article{prasad2023learning,
  title={Learning multimodal latent dynamics for human-robot interaction},
  author={Prasad, Vignesh and Heitlinger, Lea and Koert, Dorothea and Stock-Homburg, Ruth and Peters, Jan and Chalvatzaki, Georgia},
  journal={arXiv preprint arXiv:2311.16380},
  year={2023}
}

Introducing paper: MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations
Human-Robot Interaction Data

Dataset used in Bütepage, Judith, et al. "Imitating by generating: Deep generative models for imitation of interactive tasks." Frontiers in Robotics and AI 7 (2020): 47.
SCARED-C
Full name: SCARED-Corrupted

The dataset SCARED-C is introduced in the context of assessing robustness in endoscopic depth prediction models. It is part of the EndoDepth benchmark, which is designed to evaluate the performance of monocular depth prediction models specifically for endoscopic scenarios. The dataset features 16 different types of image corruptions, each with five levels of severity, encompassing challenges like lens distortion, resolution alterations, specular reflection, and color changes that are typical in endoscopic imaging.  The ground truth is on the original testing set of SCARED. 

The purpose of SCARED-C is to test the robustness of depth estimation models by exposing them to various common endoscopic corruptions. This dataset is a valuable tool for developing and evaluating depth prediction algorithms that can handle the unique challenges presented by endoscopic procedures, ensuring more accurate and reliable outcomes in medical imaging.

Introducing paper: EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction
C2A: Human Detection in Disaster Scenarios
Full name: Combination to Application

# C2A: Combination to Application Dataset

## Overview

This repository contains the code and information for the paper "UAV-Enhanced Combination to Application: Comprehensive Analysis and Benchmarking of a Human Detection Dataset for Disaster Scenarios" by Ragib Amin Nihal, Benjamin Yen, Katsutoshi Itoyama, and Kazuhiro Nakadai.

The C2A (Combination to Application) dataset is a novel synthetic dataset designed to advance human detection in disaster scenarios using UAV imagery. It combines disaster scene backgrounds from the AIDER dataset with human poses from the LSP/MPII-MPHB dataset to create a comprehensive resource for training machine learning models.

The full paper is available at: [https://arxiv.org/pdf/2408.04922](https://arxiv.org/pdf/2408.04922)

## Dataset

The C2A dataset consists of 10,215 images containing over 360,000 annotated human instances in various disaster scenarios. It includes diverse human poses (bent, kneeling, lying, sitting, upright) and disaster contexts (traffic incidents, fire, flood, collapsed buildings). 

### Usage Notes

- The dataset is split into training, validation, and test sets.
- Two annotation formats are provided for flexibility: YOLO and COCO.
- Pose information is available in a separate folder for all images.
- Users can choose between standard object detection (YOLO/COCO) or pose-aware detection (All labels with Pose info).

## Key Features

- Synthetic dataset combining real disaster scenes with human poses
- Over 360,000 annotated human instances
- 5 human pose categories
- 4 disaster scenario types
- Image resolutions ranging from 123x152 to 5184x3456 pixels
- Designed to improve human detection in complex disaster environments

Introducing paper: UAV-Enhanced Combination to Application: Comprehensive Analysis and Benchmarking of a Human Detection Dataset for Disaster Scenarios