Automated video annotation pipeline for AI training data
This technology automates the tedious process of labeling objects in video footage for training AI systems. It works by combining 3D scan data of a physical scene with standard camera video, building a virtual replica of that scene, and then transferring labels applied in the virtual world back onto the real video automatically. This eliminates the need for human annotators to manually draw bounding boxes or tags on thousands of video frames. The result is a scalable pipeline for generating high-quality labeled training datasets for computer vision models.
What you could build
A data labeling platform or pipeline tool sold to autonomous vehicle developers, robotics companies, and enterprise computer vision teams that need large volumes of accurately labeled video data without proportional human labor costs.
Who in Virginia should care
Defense primes and government contractors in Northern Virginia with computer vision and autonomous systems programs would be natural customers or partners.
Readiness: Concept
Concept — described but not yet demonstrated. Lab validated — supported by experimental results in the patent. Prototype likely — the text describes a built, working embodiment.
Readiness is inferred from the patent text, not from a lab visit.
The record
- Inventors
- Emerson Dove, Jonathan T. Black, Daniel D. Doyle
- Filed
- Patent pending — filed October 25, 2024
- Status
- Application
- Publication number
- US20250285458A1
Ready to talk?
Virginia Tech Intellectual Properties handles licensing for this technology.
Prosim summaries are generated from public patent text and are not legal advice.