Human Videos Data Collection

Project Overview:

Objective

Our goal was ambitious: to amass a comprehensive collection of human-centric videos. These videos are crucial in advancing AI technologies in areas such as facial recognition, behavioral analysis, activity recognition, and human-environment interaction.

Scope

We embarked on a journey to compile videos encapsulating the diverse spectrum of human life. Our collection spans various demographics, activities, settings, and expressions, ensuring a rich and multifaceted dataset.

Sources

Public Areas: We collaborated with city councils to source videos from bustling public spaces, all while maintaining stringent privacy standards.
Educational Institutions: Our partnerships with schools and universities allowed us to gather unique footage showcasing human interactions in academic settings.
Events: We captured the essence of human gatherings through videos from various events like weddings, sports meets, and cultural festivals.
Volunteer Contributions: A specially designed platform enabled volunteers to contribute videos, adhering to strict privacy guidelines and explicit consent protocols.

Data Collection Metrics

Total Videos Collected: 120,000
Public Area Footage: 35,000
Educational Videos: 25,000
Event Footage: 30,000
Volunteer Submissions: 30,000

Annotation Process

Stages

Demographic Data: Videos are annotated with relevant information such as age group, gender, and ethnicity of the participants.
Activity Annotation: Videos are categorized based on the primary activity observed (e.g., dancing, conversing, studying, playing).
Setting & Context: Videos are tagged with descriptors of the environment (indoors, outdoors, urban, rural) and other contextual details.

Annotation Metrics

Demographic Annotations: 120,000
Activity Annotations: 120,000
Setting Tags: 120,000

Quality Assurance

Video Quality Review: A dedicated team ensures that the videos maintain a standard of clarity, stability, and resolution.
Privacy Protocols: Rigorous checks are implemented to ensure no private or identifiable information is inadvertently captured. Faces in public spaces are blurred or anonymized unless explicit permission is obtained.
Metadata Accuracy: A subset of videos undergoes a secondary review to confirm the accuracy of annotations.

QA Metrics

Videos Enhanced for Quality: 12,000
Full-Spectrum Privacy Checks: 120,000
Metadata Audit Samples: 24,000

Conclusion

Through the Human Videos Data Collection Initiative, We has significantly contributed to the evolution of human-centric AI applications. With our extensive dataset, meticulous annotation process, and unwavering commitment to privacy and quality, we are setting new standards in AI data collection and annotation.