Skeleton-based pose annotation labeling using Amazon SageMaker Ground Truth

This article discusses a custom labeling workflow for keypoint and pose annotation using Amazon SageMaker Ground Truth. It shows how to set up and deploy the necessary components for an online web portal where a labeling workforce can annotate images using a custom UI designed for skeleton-based pose labeling.

Specifically, the article covers:

• The importance of high-quality data and reducing labeling errors for training accurate pose estimation models
• An overview of the solution architecture involving SageMaker Ground Truth, Amazon CloudFront, Amazon S3, AWS Lambda functions, and IAM roles
• Prerequisites and steps for creating the AWS CDK stack to deploy the solution
• Creating a sample labeling job and accessing the annotation results
• Customizing the labeling UI for different keypoints, skeleton structures, and colors
• Considerations for productionizing the workflow