Crowd Counting Dataset

Key Features:

• Diverse Scenarios: The dataset features crowds in various settings, from dense urban environments to open public spaces, providing a comprehensive resource for developing and testing crowd counting algorithms across different contexts.
• High-Quality Annotations: Each image in the dataset is paired with a JSON file that includes precise annotations for every individual in the crowd. The labeling details not only the count of people but also includes classification data such as gender, age group, and activity, enabling multi-dimensional analysis.
• Versatility in Applications: This dataset is ideal for training and evaluating machine learning models for applications in public safety, event management, urban planning, and retail analysis, where accurate crowd estimation and behavior analysis are critical.
• Scalable Data: With a wide range of crowd sizes, the dataset is suitable for both low-density and high-density crowd counting tasks, providing a robust foundation for developing scalable AI solutions.
• Real-World Relevance: The images are sourced from real-world environments, ensuring that models trained on this dataset can generalize well to practical applications, enhancing their reliability in real-time deployments.

Dataset Structure:

• Images: High-resolution images captured from various angles and lighting conditions.
• Annotations: JSON files containing detailed labels for each individual, including:
  • Person count
  • Classification attributes (e.g., age group, gender, activity)
  • Positional information within the image for precise localization
• Categories: The dataset is organized into different categories based on crowd density, scene type, and time of day, allowing users to focus on specific aspects of crowd analysis.