Data Scientists
Tens of hours
Per week of video footage
10-15x
Decreased processing time
Digital Signage
Industry
Berlin, Germany
Location
200
Company size
We were generating a lot of overhead trying to plug in the proprietary camera data to our open-source tool. The tool had a very limited UX and we were getting the data labeled quite slowly. Thanks to super.AI’s API import option and its very straightforward user interface, we have found a labeling platform that fits our needs much better.
Lead Machine Learning & Computer Vision Engineer

Starting point: An open source labeling tool with limited capabilities

The customer has an autonomous store solution that enables cashierless checkout. This is based on extensive research based on user interaction in store. They were using an open tool video labeling solution - CVAT - to label things like people, products and people interacting with products. They had started to look for a new platform given the limitations of the existing one: slow, buggy, long turnaround for labeling.

Simplifying labeling with super.AI

The customer reached out to super.AI given its API based solution and simple interface. They were also happy we were able to label multiple data types, not just video. We plugged in the customer’s video footage to our API and were able to label hundreds of hours of video footage for them in an easier and faster way.

Results

We were able to label hundreds of hour of video footage for the customer at a much faster turnaround time than the previous open source tool they were using and better accuracy. In fact, their video processing time decreased 10-15 times from the starting point.

We were generating lot of overhead trying to plug in the proprietary camera data to our open source tool. The tool had a very limited UX and we were getting the data labeled quite slowly. Thanks to super.AI’s API import option and its very straightforward user interface, we have found a labeling platform that fits our needs much better.

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