The system minimized to a great extent manual effort, enabling the client to generate targeted subsets of image data featuring specific objects or phenomena.
Meet our client
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Client’s Challenge
The client faced a flood of video data and needed a system to search for frames using selected tags or visual similarity. The core challenge was to reduce, organize, and label large volumes of video content, making it easier to manage and leverage for downstream applications.
Our Solution
We developed a system that automatically indexed and annotated video frames using machine learning (Image Tagging, Visual Search) and classical computer vision techniques (Image Deduplication). These enriched frames were made accessible through a web interface for easy browsing, searching, exporting, and further labeling or use in ML training/testing pipelines.
Client’s Benefits
The system minimized to a great extent manual effort, enabling the client to generate targeted subsets of image data featuring specific objects or phenomena. This not only accelerated dataset curation for modeling but also enhanced operational efficiency by several orders of magnitude.
