Tracking Data Validation Using Feature Constraints and Error Detection
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Solution Overview
Problem
Existing tracking data often contains errors that limit the accuracy and reliability of subsequent processing or visualization tasks, especially when data is combined from multiple sources.
Innovation Solution
An apparatus and method for validating tracking data by receiving samples, generating features, performing error detection using constraints, and validating the data based on the detection results to improve accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If tracking data is obtained from multiple data providers, then the quantity and coverage of tracking data increases, but the accuracy and reliability of the tracking data deteriorates due to errors in the data
Solution Approach 1:
The patent applies preliminary action by validating tracking data before it is used for processing or visualization. The system performs error detection and validation on tracking data samples prior to incorporating them into larger datasets, ensuring that only reliable data is used while still maintaining the ability to aggregate data from multiple providers.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors and validates tracking data quality. By detecting errors in tracking data and providing feedback about data quality issues, the system can adjust its validation processes and improve the reliability of aggregated tracking data from multiple sources.
2Reliability
If tracking data is validated through error detection, then the reliability of tracking data improves, but the processing time and complexity increase
Solution Approach 1:
The system performs validation and error detection as preliminary actions before the tracking data is used for processing or visualization. By validating data upfront, the system avoids time-consuming error correction processes later and ensures that only clean data enters the processing pipeline.
Solution Approach 2:
The patent extracts and removes erroneous tracking data samples from the dataset through validation processes. By identifying and taking out bad data, the system maintains the integrity of the remaining data without needing to spend excessive time correcting errors in the bulk dataset.
Data Source
AI summary
An apparatus for validation of tracking data from a data provider. The apparatus includes circuitry configured to: receive a first sample of tracking data from a data provider, the first sample of tracking data comprising information related to the location of at least one person within a physical environment at a first instance of time; generate information related to one or more features of the at least one person using the sample of tracking data; perform error detection using the information related to the one or more features of the at least one person which has been generated and one or more constraints on features of a person in the physical environment; and validate the first sample of tracking data in accordance with a result of the error detection.