Grocery Shelves Dataset

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 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.

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.

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.