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Family Member Entry/Exit and Visitor Behavior Tracking Dataset

The advantage of this dataset lies in its high data quality in terms of precision and consistency, with an annotation accuracy of over 95%. Advanced trajectory smoothing techniques are employed to make the data more accurate. This results in a noticeable improvement of 20% in intrusion detection accuracy during application, and showcases more than a 15% accuracy advantage compared to peer datasets. Its unique feature is the lightweight and non-invasive data collection method, making it suitable for various household environments. These characteristics ensure the dataset's scalability and broad applicability in other smart home systems, effectively meeting the needs of different household layouts and behavior pattern recognition requirements.

Technical Specifications

Field Type Description
file_name string File name
entity_type string Identify the type of individuals entering and leaving, such as family members or visitors.
entry_point string Record the specific location where the individual enters the room or building.
exit_point string Record the specific location where the individual exits the room or building.
duration_of_stay double The amount of time an individual stays at a location, measured in minutes.
activity_pattern string Describe the behavioral pattern of the individual, such as passing straight through, staying, or wandering.
movement_speed double The speed at which the individual moves, measured in meters per second.
interaction_count int The number of interactions the individual has with others.

Compliance Statement

Authorization Type CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial Use Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization No PII, no real company names, simulated scenarios follow industry standards
Compliance System Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com

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