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Crowd Counting Dataset

Crowd Counting Dataset is a labeled dataset including images of dense crowds with key point annotations and metadata on crowd sizes, designed for training deep learning models in crowd counting, object detection, and data analysis across a larger scale of counting datasets

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  • Photos
    647
  • People in a crowd
    1,000 - 13,000
  • Smart City
  • Computer Vision
  • Machine Learning
  • Keypoint Detection
  • Security

Crowd Counting Dataset is a labeled dataset including images of dense crowds with key point annotations and metadata on crowd sizes, designed for training deep learning models in crowd counting, object detection, and data analysis across a larger scale of counting datasets

Get in touch Download sample
  • Smart City
  • Computer Vision
  • Machine Learning
  • Keypoint Detection
  • Security
  • Photos
    647
  • People in a crowd
    1,000 - 13,000

Dataset Info

Characteristic Data
Description Crowd photos with labeling for determining crowd density and movement, and counting the number of people
Data types Image
Tasks Crowd control, object detection, and computer vision
Total number of photos 647
Type of crowd density 0-1000, 1000-2000, 2000-3000, 3000-4000, 4000-5000
Labeling Metadata (type of crowd density) and key points labeling
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Statistics

Number of images by the type of a crowd:
Number of images by the type of a crowd:

Technical
Characteristics

Characteristic Data
Image Extensions jpg
Extension of labeling file JSON
Source and collection methodology: Data was obtained by parsing photos of protests, concerts, and other mass events from the internet.

Dataset Use Cases

  • Smart City Management

    Urban Planning and Public Safety

    Crowd Counting Dataset can help city planners and safety agencies analyze crowd sizes in dense public spaces. By using counting algorithms and object detection, authorities can monitor pedestrian traffic, detect large-scale gatherings, and optimize resource allocation, creating safer and more efficient urban environments through data analysis and deep learning models.

  • Retail & Commercial Spaces

    Customer Flow and Store Analytics

    For retailers, this crowd-labeled dataset can be useful for measuring customer movement in malls, supermarkets, and events. By applying crowding counting methods, businesses can optimize store layouts, reduce congestion, and improve the shopping experience. The datasets include images of varied crowd sizes, making them ideal for building AI-driven counting systems for real-world retail analysis.

  • Event Management & Security

    Monitoring Dense Crowds in Real Time

    Event organizers and security teams rely on the human crowd dataset to track dense crowds and ensure safety. With counting datasets that include diverse crowd scenarios, AI models learn to detect the largest number of people quickly, helping reduce risks, improve surveillance, and enhance decision-making during concerts, sports, or festivals.

  • Transportation & Mobility

    Crowd Flow in Transit Hubs

    The crowd dataset supports analysis of larger-scale gatherings in airports, train stations, and bus terminals. By training deep learning models on these annotated images, transport authorities can predict congestion, optimize passenger movement, and implement crowd counting systems for smoother operations in high-density transportation networks and urban mobility planning.

FAQs

What is Сrowd Counting Dataset used for?
It is used in computer vision and deep learning research for people counting, object detection, and data analysis. It helps train counting algorithms to estimate crowd sizes, monitor dense crowds, and support larger-scale crowd management solutions.
What is included in this dataset?
The dataset contains 647 images of human crowds with densities ranging from 0–5000 people. Each image includes metadata with crowd density levels and key point labeling, making it suitable for training and evaluating counting datasets.
What types of annotations are provided?
The dataset provides metadata about crowd density categories as well as key point labeling for individuals in images.
How was the data collected?
Crowd Counting Dataset was created by parsing images from the internet, including protests, concerts, and other large-scale gatherings. This approach ensures a wide range of crowd sizes and environments, giving researchers diverse training data for crowd analysis and recognition technology.
How long does it take to receive the dataset?
The process begins once you submit a request. Our team will review the details with you and complete the necessary documents. After signing and payment, the dataset is delivered within 3–10 days.
Is this a real-world dataset or synthetic data?
Crowd Counting Dataset is a real-world dataset, not synthetic data. It contains photos of actual crowds taken from events such as protests, concerts, and other mass gatherings.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. Each dataset is fully GDPR-compliant and meets data protection obligations. All collected information comes from permissible sources.
How are Unidata datasets licensed?
Unidata datasets run on a dual licensing model: free evaluation samples are provided, while the entire dataset must be purchased.
Still have questions about using Unidata datasets? Read our user-guides

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