The People and Safety Belt Semantic Segmentation Dataset is specifically curated for industrial applications, consisting of CCTV images captured within a factory environment at a resolution of 1920 x 1080 pixels. This dataset focuses on both instance and semantic segmentation, providing annotations for people and the seat belts they are wearing, aimed at enhancing safety compliance monitoring.

CCTV Person

## Overview

CCTV Person is a dataset for object detection tasks - it contains People Detect annotations for 2,964 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).

Wheelchair People Cars

## Overview

Wheelchair People Cars is a dataset for object detection tasks - it contains Wheelchair People Cars annotations for 3,190 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).

• People and Ladders Dataset batch images cloned from:
  • https://universe.roboflow.com/chungang-university/atype-vlhbu/
  • https://universe.roboflow.com/new-workspace-wfilj/ladder-mxjbs/

Data is included in the Guide to assist services with limited experience in working with refugee young people, and to support consistent and responsive services across Victoria. It was developed as a result of discussions amongst workers from public and community sector agencies who identified gaps in the provision of service delivery to refugee young people.

Dataset

This dataset was created by Juan Estrada

Released under Apache 2.0

Contents

4,001 People Single Object Multi-view Tracking Data, the data collection site includes indoor and outdoor scenes (such as supermarket, mall and community, etc.) , where each subject appeared in at least 7 cameras. The data diversity includes different ages, different time periods, different cameras, different human body orientations and postures, different collecting scenes. It can be used for computer vision tasks such as object detection and object tracking in multi-view scenes.

gsstein/75-percent-human-dataset-og dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by JIM_BAI

Contents

Throughout the history of art, the pose—as the holistic abstraction of the human body's expression—has proven to be a constant in numerous studies. However, due to the enormous amount of data that so far had to be processed by hand, its crucial role to the formulaic recapitulation of art-historical motifs since antiquity could only be highlighted selectively. This is true even for the now automated estimation of human poses, as domain-specific, sufficiently large data sets required for training computational models are either not publicly available or not indexed at a fine enough granularity. With the Poses of People in Art data set, we introduce the first openly licensed data set for estimating human poses in art and validating human pose estimators. It consists of 2,454 images from 22 art-historical depiction styles, including those that have increasingly turned away from lifelike representations of the body since the 19^th century. A total of 10,749 human figures are precisely enclosed by rectangular bounding boxes, with a maximum of four per image labeled by up to 17 keypoints; among these are mainly joints such as elbows and knees. For machine learning purposes, the data set is divided into three subsets—training, validation, and testing—, that follow the established JSON-based Microsoft COCO format, respectively. Each image annotation, in addition to mandatory fields, provides metadata from the art-historical online encyclopedia WikiArt.

SELECTED ECONOMIC CHARACTERISTICS PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PAST 12 MONTHS IS BELOW THE POVERTY LEVEL - DP03 Universe - All families and All People Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Poverty statistics in American Community Survey (ACS) products adhere to the standards specified by the Office of Management and Budget in Statistical Policy Directive 14. The Census Bureau uses a set of dollar value thresholds that vary by family size and composition to determine who is in poverty. Further, poverty thresholds for people living alone or with nonrelatives (unrelated individuals) vary by age (under 65 Year or 65 Year and older). The poverty thresholds for two-person families also vary by the age of the householder. If a family’s total income is less than the dollar value of the appropriate threshold, then that family and every individual in it are considered to be in poverty. Similarly, if an unrelated individual’s total income is less than the appropriate threshold, then that individual is considered to be in poverty.

The European questionnaire on Information and Communication Technologies Data reveals that there exists a disparity between the frequency in which people with a low (At most lower secondary education), medium (Upper secondary and post-secondary non-tertiary education), and high (Tertiary education) formal education level use the internet. In Slovakia, 93 percent of people with a high formal education used the internet daily in 2020. At the same time only 77 percent of people with medium formal education responded that they used the internet daily. Merely 68 percent of people with lower formal education indicated that they used the internet daily, nine percent less than people with medium formal education and 25 percent less than people with high formal education. Since 2014, the share of people in all three groups that used the internet daily increased. The share of people with a low formal education level increased by 13 percent. The share of people with medium formal education increased by 22 percent since 2014. The share of people with higher education increased the slowest with six percent.

The National Library of Australia operates the "http://trove.nla.gov.au/people">Trove People and Organisations zone which allows users to access information about significant people and organisations (parties) as well as related biographical and contextual information.

The Trove People and Organisations dataset is based on the Australian Name Authority File, a unique resource maintained since 1981 by Australian libraries which contribute their holdings to "http://librariesaustralia.nla.gov.au">Libraries Australia. The Trove People and Organisations zone plays an important role in exposing records about parties and linking to them in libraries and other collecting institutions. The data also provides links to resources by and about a party and relationships between parties.

To further enrich the service the Library is collaborating with organisations that already make available information about people and organisations in their specific domains and linking to them.

The API to this dataset provides access to 885,000 identities.

Technological developments over the past few decades have changed the way people communicate, with platforms like social media and blogs becoming vital channels for international conversation. Even though hate speech is vigorously suppressed on social media, it is still a concern that needs to be constantly recognized and observed. The Arabic language poses particular difficulties in the detection of hate speech, despite the considerable efforts made in this area for English-language social media content. Arabic calls for particular consideration when it comes to hate speech detection because of its many dialects and linguistic nuances. Another degree of complication is added by the widespread practice of "code-mixing," in which users merge various languages smoothly. Recognizing this research vacuum, the study aims to close it by examining how well machine learning models containing variation features can detect hate speech, especially when it comes to Arabic tweets featuring code-mixing. Therefore, the objective of this study is to assess and compare the effectiveness of different features and machine learning models for hate speech detection on Arabic hate speech and code-mixing hate speech datasets. To achieve the objectives, the methodology used includes data collection, data pre-processing, feature extraction, the construction of classification models, and the evaluation of the constructed classification models. The findings from the analysis revealed that the TF-IDF feature, when employed with the SGD model, attained the highest accuracy, reaching 98.21%. Subsequently, these results were contrasted with outcomes from three existing studies, and the proposed method outperformed them, underscoring the significance of the proposed method. Consequently, our study carries practical implications and serves as a foundational exploration in the realm of automated hate speech detection in text.

The European questionnaire on Information and Communication Technologies Data reveals that there exists a disparity between the frequency in which people with a low (At most lower secondary education), medium (Upper secondary and post-secondary non-tertiary education), and high (Tertiary education) formal education level use the internet. In Luxembourg, 98 percent of people with a high formal education used the internet daily in 2020. At the same time 92 percent of people with medium formal education responded that they used the internet daily. However only 87 percent of people with lower formal education indicated that they used the internet daily, five percent less than people with medium formal education but 12 percent less than people with high formal education. Since 2014, the share of people in all three groups that used the internet daily increased. The share of people with a low formal education level increased by 17 percent. The share of people with medium formal education increased by six percent since 2014. The share of people with higher education increased by one percent.

The European questionnaire on Information and Communication Technologies Data reveals that there exists a disparity between the internet usage of people according to gender. This disparity although present in most countries, differs widely in its severity.

By 2019, 29 percent of male as well as 29 percent of female internet users in the European Union (EU-27) used the internet to upload self-created content. The Netherland and Denmark were among the countries with the highest rates of men and women that shared self-created content with 55 and 57 percent doing so. Finland and Belgium however were among the countries in which men and women were less likely to share their own content online.

The European questionnaire on Information and Communication Technologies Data reveals that there exists a disparity between the internet usage of people with a low, medium, and high formal education level. This disparity although present in most countries, differs widely in its severity.

In 2020, 37 percent of users with low formal education in Czechia used the internet to participate in online learning activities. Among people with medium formal education the share was 25 percent lower, amounting to only 12 percent. 34 percent of users in Czechia with a high degree of formal education had used the internet to access online learning content.

Norwegian People's Aid Activity File-LA

Relative concentration of the Southern California region's Black/African American population. The variable HSPBIPOC is equivalent to all individuals who select a combination of racial and ethnic identity in response to the Census questionnaire EXCEPT those who select "not Hispanic" for the ethnic identity question, and "white race alone" for the racial identity question. This is the most encompassing possible definition of racial and ethnic identities that may be associated with historic underservice by agencies, or be more likely to express environmental justice concerns (as compared to predominantly non-Hispanic white communities). Until 2021, federal agency guidance for considering environmental justice impacts of proposed actions focused on how the actions affected "racial or ethnic minorities." "Racial minority" is an increasingly meaningless concept in the USA, and particularly so in California, where only about 3/8 of the state's population identifies as non-Hispanic and white race alone - a clear majority of Californians identify as Hispanic and/or not white. Because many federal and state map screening tools continue to rely on "minority population" as an indicator for flagging potentially vulnerable / disadvantaged/ underserved populations, our analysis includes the variable HSPBIPOC which is effectively "all minority" population according to the now outdated federal environmental justice direction. A more meaningful analysis for the potential impact of forest management actions on specific populations considers racial or ethnic populations individually: e.g., all people identifying as Hispanic regardless of race; all people identifying as American Indian, regardless of Hispanic ethnicity; etc. "Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as HSPBIPOC alone to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region that identify as HSPBIPOC alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of HSPBIPOC individuals compared to the Southern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HSPBIPOC individuals are highly concentrated locally.

A People St Pedestrian Plaza creates accessible public open space by closing a portion of street to vehicular traffic. A colorful, patterned treatment is applied to the street surface; while large planters and other elements define the Plaza perimeter. The Community Partner maintains and operates the Plaza, providing movable tables and chairs, public programs, and ongoing neighborhood outreach. People Street Pedestrian Plazas must remain publicly accessible at all times.You can refer to the People St projects map to locate People St projects installed or coming soon within the City of Los Angeles.