Video · Multimodal / AI & Machine Learning
Industrial First-Person Video Dataset | 50,000 Hours of Warehouse & Workplace Tasks - Egocentric Data
Advertised by providerLicensing rights not independently verifiedPrice on request
Overview
Industrial First-Person Video Dataset | 50,000 Hours of Warehouse & Workplace Tasks - Egocentric Data is a multimodal dataset. The provider states a volume of 50,000 hours. Typical applications include alignment & RLHF and robotics & embodied AI. Relevant sectors: AI & Machine Learning, Education and Manufacturing & Industrial. Pricing is not published and is provided on request.
Primary applications
What the listing offers
- Stated volume of 50k hours.
The Industrial First-Person Video Dataset is a 50,000-hour collection of egocentric workplace video capturing real industrial, warehouse, logistics, retail, manufacturing, and commercial operations tasks from the human point of view. The dataset is designed for Physical AI, robotics, industrial AI, workplace automation, safety monitoring, operations intelligence, and egocentric action recognition. The dataset captures how workers move through operational environments, interact with tools and equipment, scan barcodes, pick and pack items, handle boxes, stock shelves, inspect products, sort packages, process returns, perform inventory checks, complete assembly steps, and clean commercial spaces. Because the footage is recorded from a first-person perspective, it preserves hand-object interactions, tool usage, spatial navigation, workflow order, object placement, and real-world task constraints. Task coverage includes warehouse picking and packing, barcode scanning, shelf stocking, pallet handling, inventory checks, forklift-adjacent workflows, package sorting, quality inspection, returns processing, manufacturing assembly, janitorial workflows, commercial cleaning, equipment checks, and workplace organization. Technical specifications include 50,000 hours of first-person industrial video, 1080p or higher video resolution, warehouse and workplace environment coverage, task labels, action segment labels, object labels, tool labels, location-zone labels, hand-object interaction la
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