The growing demand for new content drives the exponential growth of image and video data. Although more content is being produced, the expectations of users and content creators often don’t align with today’s systems. Media companies and archives need intelligent solutions to provide their users with a better search experience and retain the knowledge of their own data, while also speeding up video production and making them more cost-effective.
Up until today, media companies had to tag and classify all incoming video and image assets manually to make the process of pre-selection easier. In addition to the time-consuming task of manually tagging each file, maintaining a consistent keywording and categorization process is a challenge.
DeepVA’s AI solution for image and video recognition enables the automatic assignment of relevant tags or keywords to extensive image and video collections. Our advanced deep-learning models are a core feature of DeepVA’s AI platform, which uses a visual mining technology to analyze images, videos, and live streams on a pixel level, extracting their features and detecting relevant personalities, objects, points of interest, text insertions, and many other relevant features. 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.
With DeepVA, we can help your employees with redundant and complex processes and support your company in building automated and customer-centric products.
faster data acquisition
With DeepVA, we have found a great partner with major know-how in computer vision that carries the latest research data in its DNA. Practicality and interdisciplinarity are very important to us, so we are looking forward to future innovative and exciting projects with DeepVA.
With our expertise, we will help you find a perfect solution for your needs!
Integration of image and video recognition into a MAM
(media asset management) system.