This is the dataset containing all of the derivatives from the Cambridge Centre for Ageing and Neuroscience to evaluate the scientific utility of the services on the brainlife.io platform and to build reference datasets for the brainlife.io manuscript.

Aging impacts the brain's structural and functional organization and over time leads to various disorders, such as Alzheimer's disease and cognitive impairment. The process also impacts sensory function, bringing about a general slowing in various perceptual and cognitive functions. Here, we analyze the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state magnetoencephalography (MEG) dataset—the largest aging cohort available—in light of the quasicriticality framework, a novel organizing principle for brain functionality which relates information processing and scaling properties of brain activity to brain connectivity and stimulus. Examination of the data using this framework reveals interesting correlations with age and gender of test subjects. Using simulated data as verification, our results suggest a link between changes to brain connectivity due to aging and increased dynamical fluctuations of neuronal firing rates. Our findings suggest a platform to develop biomarkers of neurological health.

This is the dataset containing all of the derivatives from the Cambridge Centre for Ageing and Neuroscience dataset to evaluate the validity of the services for MEG data on the brainlife.io platform.

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.

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.

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.

URL: http://mmas.comp.nus.edu.sg/MCAD/MCAD.html

Resources:

• IDXXXX.mp4.tar.gz contains video data for each individual
• boundingbox.tar.gz contains person bounding box for all videos
• protocol.json contains the evaluation protocol
• img_list.txt contains the download URLs for the images version of the video data
• idt_list.txt contians the download URLs for the improved Dense Trajectory feature
• stip_list.txt contians the download URLs for the STIP feature

• Manual annotated 2D joints for selected camera view and action class (available via http://zju-capg.org/heightmap/)

How to Cite:

Please cite the following paper if you use the MCAD dataset in your work (papers, articles, reports, books, software, etc):

• Wenhui Liu, Yongkang Wong, An-An Liu, Yang Li, Yu-Ting Su, Mohan Kankanhalli
  Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking
  IEEE Winter Conference on Applications of Computer Vision (WACV), 2017.
  http://doi.org/10.1109/WACV.2017.28

Data Cam

## Overview

Data Cam is a dataset for object detection tasks - it contains 0 1 2 3 annotations for 321 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Myelin maps in subject-space in the CamCAN dataset

CamCAN: release004/BIDS_20190411/anat ; inputs are defined in the JSON file

MRI preprocessing: T1 and T2 images were skull-stripped (mri_synthstrip), bias corrected (N4BiasFieldCorrection), intensity normalized (ImageMath), and resliced/resampled to T1 space when needed (mri_vol2vol)

Myelin mapping: T1w/T2w ratio based on code hosted at brainlife.io (https://brainlife.io/app/60355b8a3a0011acbb52c3c5)

OUTPUT:

myelin_maps_subjects.txt: List of subject IDs. Myelin maps were concatenated in this order

myelin_maps_652subj_T1space.nii.gz: Myelin maps in subject-space, concatenated along the 4th dim. Myelin values may be scaled between 0 and 5 for visualization purposes.

Dependencies:

freesurfer: freesurfer-linux-ubuntu22_x86_64-7.4.1-20230614-7eb846

ANTs: version 2.5.0.post9-gc40a681

connectome workbench: version 2.0.1

The number of subjects per age group are bifurcated by sex via the convention (male, female). *The 1000FCP subjects in Group #1 were, more precisely, in the 21–30 range.

Data and scripts from:Ageing and the ipsilateral M1 BOLD response: a connectivity study By Yae Won Tak, Ethan Knights, Richard Henson and Peter Zeidman(Revision 1)Analysis of the Cam-CAN dataset using DCM for fMRI, investigating why right primary motor cortex (M1) has negative BOLD responses that decrease in amplitude with age.

Collection description

Contrasts from the sensori-motor task of the Camcan dataset

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

single-subject

Map type

Z

Dataset Card for trail-camera

** The original COCO dataset is stored at dataset.tar.gz**

  Dataset Summary

trail-camera

  Supported Tasks and Leaderboards

object-detection: The dataset can be used to train a model for Object Detection.

  Languages

English

  Dataset Structure





  Data Instances

A data point comprises an image and its object annotations. { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB… See the full description on the dataset page: https://huggingface.co/datasets/Francesco/trail-camera.

Skin Cam

## Overview

Skin Cam is a dataset for object detection tasks - it contains Skin annotations for 297 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Home Cam

## Overview

Home Cam is a dataset for object detection tasks - it contains Persons F6BD annotations for 585 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Web Camera People Behavior Dataset for computer vision tasks

Dataset includes 2,300+ individuals, contributing to a total of 53,800+ videos and 9,300+ images captured via webcams. It is designed to study social interactions and behaviors in various remote meetings, including video calls, video conferencing, and online meetings.

By leveraging this dataset, developers and researchers can enhance their understanding of human behavior in digital communication settings, contributing to advancements in technology and software designed for remote collaboration. - Get the data

Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F5d15deaf6757f20132a06e256ce14618%2FFrame%201%20(9).png?generation=1743156643952762&alt=media" alt="">

Dataset boasts an impressive >97% accuracy in action recognition (including actions such as sitting, typing, and gesturing) and ≥97% precision in action labeling, making it a highly reliable resource for studying human behavior in webcam settings.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Researchers can utilize this dataset to explore the impacts of web cameras on social and professional interactions, as well as to study the security features and audio quality associated with video streams. The dataset is particularly valuable for examining the nuances of remote working and the challenges faced during video conferences, including issues related to video quality and camera usage.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

Hole Cam

## Overview

Hole Cam is a dataset for object detection tasks - it contains Buche annotations for 665 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A resting-state functional connectivity (rsFC)-constructed functional network (FN) derived from functional magnetic resonance imaging (fMRI) data can effectively mine alterations in brain function during aging due to the non-invasive and effective advantages of fMRI. With global health research focusing on aging, several open fMRI datasets have been made available that combine deep learning with big data and are a new, promising trend and open issue for brain information detection in fMRI studies of brain aging. In this study, we proposed a new method based on deep learning from the perspective of big data, named Deep neural network (DNN) with Autoencoder (AE) pretrained Functional connectivity Analysis (DAFA), to deeply mine the important functional connectivity changes in fMRI during brain aging. First, using resting-state fMRI data from 421 subjects from the CamCAN dataset, functional connectivities were calculated using sliding window method, and the complex functional patterns were mined by an AE. Then, to increase the statistical power and reliability of the results, we used an AE-pretrained DNN to relabel the functional connectivities of each subject to classify them as belonging to the attributes of young or old individuals. A method called search-back analysis was performed to find alterations in brain function during aging according to the relabeled functional connectivities. Finally, behavioral data regarding fluid intelligence and response time were used to verify the revealed functional changes. Compared to traditional methods, DAFA revealed additional, important aged-related changes in FC patterns [e.g., FC connections within the default mode (DMN) and the sensorimotor and cingulo-opercular networks, as well as connections between the frontoparietal and cingulo-opercular networks, between the DMN and the frontoparietal/cingulo-opercular/sensorimotor/occipital/cerebellum networks, and between the sensorimotor and frontoparietal/cingulo-opercular networks], which were correlated to behavioral data. These findings demonstrated that the proposed DAFA method was superior to traditional FC-determining methods in discovering changes in brain functional connectivity during aging. In addition, it may be a promising method for exploring important information in other fMRI studies.

Collection description

Contrasts from the sensori-motor task of the Camcan dataset

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

single-subject

Map type

Z

Cam Ball

## Overview

Cam Ball is a dataset for object detection tasks - it contains Cam annotations for 322 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Quad Cam Pedestrian

## Overview

Quad Cam Pedestrian is a dataset for object detection tasks - it contains Person annotations for 400 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Diff Cam

## Overview

Diff Cam is a dataset for instance segmentation tasks - it contains Cement_bag annotations for 5,059 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Merge Iphone Cam

## Overview

Merge Iphone Cam is a dataset for object detection tasks - it contains Normally Abnomaly 4TIq MH3C Good U2hA annotations for 1,621 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).