Human Body High Precision Segmentation Dataset

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Project Overview:

Objective

Our goal was to develop a dataset that sets new benchmarks in high precision segmentation of the human body. This invaluable resource is now empowering advancements in medical imaging, high-end visual effects, augmented reality, and advanced body tracking systems.

Scope

Gather an exhaustive range of human body images showcasing varied poses, clothing types, body shapes, and sizes. Annotations will pinpoint even the subtlest features of the body.

Sources

Studio captures with high-resolution cameras, employing neutral backgrounds.
Additionally Outdoor and indoor candid shots representing different activities.
Furthermore Medical imaging (where available and ethical).
Moreover User-contributed images to capture diversity.
Lastly Sports and fitness imagery to represent varied body poses and movements.

Data Collection Metrics

Total Data Points: 400,000 images
Studio Captures: 150,000
Candid Shots: 100,000
Medical Imaging: 30,000
User-contributed Images: 90,000
Sports and Fitness: 30,000

Annotation Process

Stages

Raw Data Refinement: To initiate the process, we meticulously sifted through the raw data, systematically eliminating any images that fell short of our stringent standards, whether due to blurriness or incomplete shots.
Primary Body Segmentation: Following this initial refinement, our expert team proceeded to segment the main body regions across all 400,000 images, including the torso, arms, legs, and head.
High Precision Annotation: Subsequently, we delved deeper into the data, meticulously annotating finer details such as fingers, toes, facial features, and distinct body marks, resulting in a total of 2,400,000 detailed segments.
Quality Assurance: Finally, we conducted an exhaustive quality assurance process, meticulously reviewing the fidelity of annotations across 80,000 images, which constituted 20% of the total dataset.

Annotation Metrics

Total Primary Segmentations: 400,000
High Precision Annotations: 2,400,000 (6 detailed segments per image on average)
Annotations Reviewed: 80,000 (20% of total segmentations for quality assurance)

Quality Assurance

Stages

Expert Validation: Furthermore, our annotations were rigorously vetted by anatomists and visual effects professionals.
In addition Automated Checks: Advanced software solutions were deployed to catch any inconsistencies or errors.
Moreover Collaborative Consistency: Multiple annotators worked on overlapping sets to guarantee uniformity.

QA Metrics

Annotations Vetted by Experts: 80,000 (20% of total annotations)
Inconsistencies Identified and Rectified: 8,000 (2% of total annotations)

Conclusion

Our journey in creating this dataset exemplifies our expertise and commitment to excellence in data collection and annotation. Furthermore, we are thrilled to offer this dataset as part of our broader suite of solutions. Additionally, we are eager to see the transformative impact it will have across various industries.