Annotating the Movement of People in Videos Using Keypoints
Client Profile
Industry: Technology services
Location: USA
Size: 10-50
Company Bio
The client has a suite of products that help companies create and manage the huge amounts of customized datasets they need to build algorithms. They use AI to make customized AI training data for large tech companies to help their clients develop machine learning-based features in their products multiple times faster than they could themselves.
Overview
Marking the human skeleton with key points on each frame in a video.
12,000
videos annotated
115
full-time data annotators
>95%
quality score
Business Challenge
The client needed to annotate the human skeleton with key points on each frame in a video which is very challenging because the person is constantly moving in the video and therefore the combinations of points are constantly changing. Moreover, the people in the video can be in various positions i.e. vertical (running, jumping, squatting), horizontal (push-ups, laying down, plank position). Moreover, the algorithm evaluates key points differently when a person is in different positions.
Solutions Provided by Mindy Support
Mindy Support assembled a team of 115 full-time data annotators to perform all of the needed labeling tasks. In total we annotated 12,000 videos which is 1,200,000 frames. All of this work was done within the specified time frame of 60 days and the quality score was >95%.
Mindy Support is ISO 9001 and ISO 27001 certified. Our information security management system (ISMS) is built on the basis of international standards that helps organizations keep information assets secure.
We are committed to respecting all rights of the data subjects under the General Data Protection Regulation (GDPR) (EU) 2016/679. According to the Article 28 of the GDPR, the relationship between the controller and the processor are regulated by Data Processing Agreement, which we put in place with every Client.
Results Delivered to the Client
12,000 videos annotated in total
1,200,0000 frames were annotated
Quality score >95%
All of the work was completed in 60 days
12,000
videos annotated
115
full-time data annotators
>95%
quality score
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