The increasing amount of visual data poses a major challenge for many companies, especially when it comes to retrieving specific people or places in images or videos. Among countless data, the search usually turns out to be like the famous needle in a haystack. This not only means high costs but also many hours of redundant work like manually tagging files, maintaining consistent keywording and an exhausting categorization process.
So how can DeepVA help you find the people or places you’re looking for faster and more efficient?
DeepVA’s AI solution for image and video recognition enables the automatic assignment of relevant tags or keywords to extensive image and video collections. The AI models have been trained with more than 3,000 objects, over 20,000 personalities, as well as more than 30,000 landmarks and 300,000 logo variations from day-to-day life, and can additionally be individualized with custom tags to achieve the highest accuracy. This not only reduces redundant and complex processes but provides an automated environment for all the searches of the users.
faster data acquisition
COST REDUCTION
faster labelling
What results can be obtained?
Extensive extraction of a wide range of metadata, with frame accuracy.
Low staff effort compared to manual keywording.
Existing archives benefit from significantly improved searchability and new discoveries in their collection.
Improving visual search
Face Recognition detects and identifies the faces of public figures in a variety of categories such as politics, sports, business and entertainment.
Landmark Recognition identifies all important sights, architectural structures and natural monuments across Europe. Easily archive and retrieve visual material showing places of interest for content creation.
Object and scene recognition detects and labels various objects and scenes, from general to more specific ones.
Requirements
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A product of «The Chainless».
Designed and engineered in Germany.
The Chainless GmbH
Wannerstraße 19
79106 Freiburg
Germany
The project has indirectly received funding from the European Commission’s Horizon 2020 Framework Programme through the STADIEM project (Grant Agreement 957321)
