Human Body Segmentation Dataset

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Project Overview:

Objective

To develop a high-quality dataset for human body segmentation, which is essential for advancing computer vision applications like augmented reality, virtual try-on systems, and video game development, several steps must be taken.

Scope

We are looking for a collection of diverse human body images in various environments, poses, and lighting conditions. Moreover, these images should include detailed annotations to clearly distinguish individual body parts and ensure data accuracy.

Sources

Studio portraits thoughtfully collected with controlled lighting, successfully curated for visual precision.
Outdoor shots meticulously gathered in different weather and lighting conditions, thoughtfully curated for variety.
Images from sports events capturing dynamic poses that have been carefully collected and successfully curated.
Indoor household settings successfully collected and organized.
Crowd images from public events thoughtfully gathered and successfully curated for comprehensive representation.

Data Collection Metrics

Studio Portraits: 50,000
Outdoor Shots: 70,000
Sports Event Images: 40,000
Indoor Settings: 90,000
Crowd Images: 50,000

Annotation Process

Stages

Raw Data Cleaning: First, ensure all images are clear and free of blurs. Additionally, remove any duplicate images.
Boundary Annotation: Next, draw boundaries around the entire human body.
Body Parts Segmentation: Then, detail individual body parts such hair and skin and if applicable, distinguish between clothing, accessories and footwear.
Clothing Segmentation: If applicable, distinguish between different clothing items like shirts, trousers, dresses, shoes, and so on.
Annotation Review: Finally, conduct a review to check for and fix any inconsistencies.

Annotation Metrics

Total Annotations: 1,200,000 (4 segments per image on average)
Boundary Annotations: 300,000
Body Parts Annotations: 600,000
Clothing Annotations: 240,000
Annotations Reviewed: 60,000

Quality Assurance

Stages

Expert Review: Segmentation experts examined random samples of the annotations to ensure accuracy.
Automated Checks: Algorithms flagged possible overlaps or missing segments for further inspection.
Inter-annotator Agreement: Some tasks were given to multiple annotators to verify consistency and reliability.

QA Metrics

Annotations Reviewed by Experts: 120,000 (10% of total annotations)
Inconsistencies Identified and Rectified: 12,000 (1% of total annotations)

Conclusion

The Human Body Segmentation Dataset is a key resource for advancements in computer vision related to the human form. It ensures precision by covering a wide range of human poses, clothing, and environments.