Weapon Detection Dataset

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 egocentric dataset contains first-person videos of daily activities in home environments, designed for Physical AI, robotic systems, manipulation tasks, and egocentric vision research. The videos were captured using 2 hardware configurations: Pico + Motion Trackers (2,321 hours) and Zed + Pico + Motion Trackers (1,729 hours). Quaternion-based orientation supports 3D pose estimations and egocentric tracking from first-person.

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.

Contains 10,000 high-resolution videos of staged human fall events recorded across indoor and outdoor settings using static and moving cameras. Designed for fall detection and computer vision research, this camera dataset provides well-structured metadata, realistic fall scenarios, and consistent 1080p video quality for training and evaluating detection systems.

This fight detection dataset contains 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.

Lerobot SO-101 dataset is a compact, high-quality set of 20 teleoperated robotic arm recordings (~30 FPS) captured on the calibrated robot studio SO-101 arm using multi-view cameras (front, wrist, top) in the Lerobot framework. Designed for imitation learning and model training on the Lerobot platform, it demonstrates pick-and-place and basic manipulation tasks with structured LeRobot v2.1 annotations.

This dataset is intended as a limited reference set and contains 20 episodes; however, full custom data collection in Lerobot format can be provided on request.

This droid dataset includes recordings of cleaning, laundry folding, dishwashing activities, combining head-mounted video with detailed robotic data streams from seven 9-axis IMU units placed on the chest, upper arms, forearms, wrists. The robot dataset captures data from 3-axis accelerometers, 3-axis gyroscope, and 3-axis magnetometer sensors, using onboard sensor-fusion to generate orientation quaternions for motion tracking and robotic learning.

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.