Old Person and Children Contour Segmentation Dataset
Project Overview:
Objective
Scope
To gather a varied collection of images featuring the elderly and children in different settings and poses, every image will have pixel-wise annotations that precisely define the contours of these individuals, distinguishing them from other elements within the frame.
Sources
- To gather a varied collection of images featuring the elderly and children in different settings and poses, we need to consider several key factors. First and foremost, the images should encompass a wide range of environments to provide a comprehensive dataset. These settings might include indoor environments such as homes, classrooms, and senior centers, as well as outdoor locations like parks, playgrounds, and city streets. Each setting will contribute unique contextual elements that will be useful for different applications.
Data Collection Metrics
- Total Images: 18,000
- Elderly Individuals: 9,000
- Children: 9,000
Annotation Process
Stages
- Image Pre-processing: To begin with, adjustments for light balance, resolution enhancement, and normalization are performed to guarantee consistent image quality.
- Pixel-wise Segmentation: Next, using advanced software tools, annotators meticulously demarcate every pixel to trace the contours of elderly individuals and children.
- Validation: Finally, each annotated image undergoes review by a second annotator to ensure utmost accuracy.
Annotation Metrics
- Total Pixel-wise Annotations: 18,000 (One for each image)
- Average Annotation Time per Image: 20 minutes (Given the intricacy of human contours)
Quality Assurance
Stages
Automated Model Cross-check: Preliminary contour segmentation models validate the consistency and accuracy of annotations.
Peer Review: Additionally, expert annotators randomly review images to uphold a benchmark of quality.
Inter-annotator Agreement: Moreover, certain images are re-annotated by different professionals to ensure uniformity in the segmentation process.
QA Metrics
- Firstly, Annotations Validated by Models: 9,000 (50% of total images).
- Additionally, Peer-reviewed Annotations: 5,400 (30% of total images).
- Moreover, Inconsistencies Identified and Rectified: 540 (3% of total images).
Conclusion
The Old Person and Children Contour Segmentation Dataset is a pivotal initiative in the world of computer vision, especially catering to age-specific applications. Whether it’s for healthcare monitoring, interactive educational tools for children, or ensuring the safety of vulnerable populations, this dataset sets the stage for remarkable technological advancements catered to both our young and elderly.
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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