Waste Bin Fill Level Dataset

Abandoned object detection dataset includes 1,062 Full HD videos featuring 50 object types across diverse public scenes. Recorded at 30 FPS with metadata covering camera and scene conditions, it provides video data for developing computer vision models that identify unattended objects such as bags and luggage in surveillance footage.

Graffiti dataset contains 550 Full HD videos of graffiti drawings captured outdoors from a static camera at 30 FPS, across approximately 30–40 locations. With metadata covering action type, camera height, distance, lighting, time of day, and people count, this graffiti dataset supports computer vision, object detection, and graffiti detection systems for smart city and public safety applications.

This dataset contains 3D scans of apartments, including splats and meshes generated via 3DGS technology, with high-quality point clouds, lidar scans, RGB-D data, and textured 3D meshes captured using the XGRIDS PortalCam. Designed for robotics, physical AI, and robot training, this real-world 3DGS dataset supports 3D reconstruction, scene understanding, object detection, spatial analysis, and deep learning workflows for indoor environments.

This car accident dataset contains 5,000 annotated video clips capturing 3 seconds before, during, and after traffic accidents, providing structured data for car accident detection and prediction tasks. This video dataset supports training models for traffic detection, road accident analysis, and crash recognition, offering consistent MP4 footage for developing reliable accident detection systems.

Scene Scanning Video Dataset includes 10,000 high-resolution (1920×1080, 30 FPS) MP4 videos capturing people looking around and scanning their environment in controlled settings. This video dataset contains annotated metadata labels—such as environment type, camera motion, participant count, gender, duration, and resolution—providing structured training data for computer vision, face detection, face recognition, and object detection.

Fall detection dataset contains 1,047 high-resolution videos of staged human falls recorded in controlled indoor and outdoor settings, including parking areas, rooms, and corridors. Videos were captured from static and moving camera perspectives at 1920×1080 resolution, supporting fall detection, computer vision research, and public safety applications. The dataset covers ~40–50 locations, with clothing changed every 10 videos.

High-quality weapon detection dataset containing 10,000 annotated images of staged individuals with visible weapons, collected from public CCTV footage and internet sources for training weapon detection models. This surveillance dataset includes bounding box annotations and rich metadata for weapons detection, small object detection, and real-time security systems across CCTV and camera-based monitoring scenarios.

This fight detection dataset contains 3,010 videos of simulated fights involving 2–4+ participants across 1v1, 2v1, 2v2, and group scenarios.  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.

It is a fire and smoke detection dataset containing 292 videos recorded at 37 different locations with a fixed camera setup (3 m height) at two distances: close (3–5 m) and far (10–15 m). Each location is filmed during the day, with 24 locations also filmed in the evening and/or at night. Every video is annotated with condition (fire or smoke), camera distance, time of day and location. Suitable for training fire and smoke detection models for early-warning and public-safety video analytics.