Topological Crowd Analysis for Dangerous Congestion Detection
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Solution Overview
Problem
Existing methods struggle to accurately distinguish between dangerous and safe congestion in crowded areas, leading to false alarms and increased workload, as they rely on density thresholds or image-based tracking, which are inefficient and prone to errors.
Innovation Solution
An information processing method that uses topological data analysis to generate feature amount data from position data, learning a model to analyze spatial features and determine the presence of dangerous congestion without relying on density or individual tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If density threshold methods are used to detect congestion, then the detection process is simple, but the accuracy of distinguishing dangerous from safe congestion is poor leading to false alarms
Solution Approach 1:
The patent replaces traditional mechanical/density-based detection methods with topological data analysis. Instead of using simple density thresholds, the system uses topological features (connectivity, holes, components) extracted from point cloud data to detect dangerous congestion, achieving higher accuracy without proportionally increasing system complexity
Solution Approach 2:
The patent changes the detection parameters from density-based metrics to topological metrics. By using Betti numbers and topological features that capture the spatial structure and connectivity of crowds, the system can distinguish dangerous congestion patterns that density alone cannot detect
2Reliability
If image-based tracking methods are used to monitor crowd density, then individual movement can be tracked, but the processing load increases and errors occur
Solution Approach 1:
The patent extracts only the necessary positional information from crowd data to create point cloud representations, discarding unnecessary individual identification and tracking information. This extraction approach maintains detection reliability while significantly reducing processing load compared to full image-based tracking
Solution Approach 2:
The patent creates simplified point cloud copies of crowd positions instead of processing full images or detailed tracking data. These point cloud representations capture the essential spatial distribution and connectivity information needed for dangerous congestion detection while being much more computationally efficient
3Ease of operation
If simple density thresholds are used, then the system is easy to operate, but false alarms increase and workload increases
Solution Approach 1:
The patent adds topological dimensions to the congestion detection analysis. By considering not just density but also connectivity (Betti numbers), spatial components, and topological features, the system captures richer information about crowd structure without complicating operation. The automated topological analysis maintains ease of use while preventing information loss
Data Source
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AI summary
An information processing program causes a computer to execute a process, the process including: obtaining a plurality of position data indicating positions of moving objects on an area of interest at different time points; generating first feature amount data representing spatial features on the area, the first feature amount data corresponding to a diagram generated by topological data analysis based on the obtained plurality of position data, the diagram representing a timing at which each of one or more different types of shapes that are formable by a combination of the positions of the moving objects on the area appears and disappears depending on a change in resolution; and learning, based on the generated first feature amount data, a model that outputs a result of analyzing a danger brought about by a congested state on the area according to input feature amount data representing the spatial features on the area.