Weapon Detection Dataset

Fall Detection Dataset contains 10,000 high-resolution videos of staged human fall events recorded across indoor and outdoor settings using static and moving cameras. This camera dataset supports fall detection computer vision research by providing well-structured metadata, realistic fall scenarios, and consistent 1080p video quality for training and evaluating detection systems.

This fight detection dataset contains 1,000 high-quality videos of simulated physical altercations recorded in controlled environments, captured from static and moving surveillance camera views at up to 1920×1080 resolution and 30 FPS. Designed for violence detection, action recognition, and public safety systems, this surveillance dataset includes rich metadata annotations enabling accurate camera fight analysis and training violence detection models.

This Serbian road traffic dataset contains 5,000 high-resolution (≥1080p) videos and annotated frame images with JSON bounding box annotations for vehicle detection and classification tasks. Collected from bridge vantage points, it provides detailed data on traffic flows, vehicle types (cars and minivans), and traffic density, supporting AI development for traffic prediction, road safety analysis, and computer vision model training.

This smoke and fire detection dataset offers 85 high-quality RGB videos in MP4 format with JSON annotations, including frame numbers, object coordinates, and classes for fire and smoke. The dataset supports object detection, fire and smoke recognition, and computer vision tasks, enabling deep learning models for real-time monitoring, early detection, and efficient fire management under varied environmental conditions.

Real-Time Traffic Video Dataset is a traffic dataset featuring annotated video streams from traffic cameras at urban intersections and crosswalks, designed for vehicle detection, pedestrian detection, and traffic monitoring in real-time traffic scenarios with metadata on weather, time, and traffic events to support advanced traffic surveillance and traffic prediction models.

It is a collection of water meter images with segmentation masks and OCR-labeled meter readings, designed to support detection and meter reading tasks in AI models for monitoring water usage, improving customer service, and enhancing wastewater treatment and water supply systems.

It is a labeled supermarket shelves dataset containing over 5,000 high-quality shelf images from grocery stores, designed for product detection, object recognition, and image classification tasks in computer vision models, with annotations for facing, flipped, and occluded grocery items to support retail automation and grocery delivery applications.

It is a garbage classification dataset consisting of labeled images of garbage cans in various states - full, empty, and scattered - designed to train classification models and detection systems for trash classification, waste sorting, and garbage collection tasks using deep learning and machine learning techniques.

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