Pedestrian Source Sink Assignment via Vector Correlation
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Solution Overview
Problem
Current pedestrian tracking methods face challenges in accurately assigning sources and sinks in complex environments due to inaccessibility, distance, and obstacles, leading to inefficient manual processes for large datasets.
Innovation Solution
A method and device for automatically assigning sources or sinks to routes by defining location data, monitoring individual movements, generating routing data, determining movement vectors, correlating vectors with distance vectors, and normalizing scalar products to assign likely sources or sinks based on correlation results, while excluding inaccessible or distant options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tracking is used to process large amounts of pedestrian data, then productivity is improved, but measurement precision deteriorates due to inaccessibility, distance, and obstacles
Solution Approach 1:
The patent introduces correlation results as an intermediary mechanism between movement vectors and distance vectors. By calculating correlation coefficients that quantify the relationship between these vectors, the system can objectively determine source-sink assignments without manual intervention, thereby maintaining high productivity while improving measurement precision through mathematical rigor
Solution Approach 2:
The patent replaces manual mechanical assignment processes with automated computational methods. Instead of human editors manually analyzing each pedestrian trace, the system uses algorithms that automatically calculate movement vectors, distance vectors, and their correlations to assign sources and sinks, dramatically increasing productivity while maintaining or improving precision through consistent mathematical application
2Measurement precision
If manual assignment is used for source and sink identification, then measurement precision may be maintained through expert judgment, but productivity deteriorates due to time-consuming individual analysis
Solution Approach 1:
The patent enables the system to perform self-service by automatically calculating movement vectors from tracked positions, computing distance vectors to potential sources and sinks, and determining correlation coefficients without human intervention. This automation allows the system to process large datasets independently, dramatically improving productivity while maintaining precision through objective mathematical calculations
Solution Approach 2:
The patent transforms the manual assignment process into an automated parameter-based system. By changing from subjective human judgment to objective mathematical parameters (movement vectors, distance vectors, correlation coefficients), the system achieves both high productivity through automation and high precision through consistent mathematical application
3Productivity
If automated tracking processes large datasets, then productivity is improved, but device complexity increases due to multiple data processing steps
Solution Approach 1:
The patent segments the complex assignment process into distinct, manageable steps: (1) calculating movement vectors from tracked positions, (2) calculating distance vectors from potential sources and sinks, (3) computing correlation coefficients between these vectors, and (4) assigning sources and sinks based on correlation thresholds. This segmentation makes the complex system more manageable and maintainable while preserving high productivity
Data Source
AI summary
A method for assigning a source or a sink to a route of an individual has the steps: defining source/sink location data indicating possible sources and/or sinks in a monitored compound, monitoring a route of a moving individual in the monitored compound, generating routing data from the monitored route with initial and terminal location data. After determining an initial and/or a terminal movement vector from the initial and/or the terminal location data, a plurality of initial distance vectors between each of the source location data and the initial location data and/or a plurality of terminal distance vectors between each of the sink location data and the terminal location data are determined, which are correlated with each of the initial distance vectors and/or terminal distance vectors in order to assign respective source location data and/or sink location data to the monitored route on the basis of the correlation results.