RF Sensor Network for Privacy-Safe Crowd Density Estimation
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Solution Overview
Problem
Existing methods for monitoring crowd movements, such as camera systems and wireless sensor networks, face challenges like privacy concerns, false estimations, and inefficiencies in low-light conditions, and fail to timely detect potentially dangerous crowd density changes.
Innovation Solution
A computer-implemented method using a wireless sensor network with nodes exchanging RF signals to measure attenuations and estimate crowd flow and density between subregions, allowing for accurate monitoring without wearable devices or cameras, and enabling timely detection of unsafe situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical cameras are used for crowd monitoring, then crowd presence and motion can be detected, but privacy issues arise and false estimations occur in low-light conditions
Solution Approach 1:
The patent replaces optical camera systems with a wireless sensor network that uses RF signal propagation and RSS measurements to detect crowd presence and motion. This substitution eliminates privacy concerns associated with visual identification while maintaining detection capabilities through signal attenuation measurements caused by crowd density.
Solution Approach 2:
The invention changes the detection parameter from optical intensity (camera-based) to radio signal attenuation (RF-based). By measuring RSS values and their attenuation patterns, the system achieves accurate crowd detection independent of lighting conditions, resolving the low-light performance issue while avoiding privacy intrusion.
2Object-affected harmful factors
If wireless sensor networks are used for crowd monitoring, then privacy is preserved and low-light detection is improved, but timely detection of dangerous crowd density changes is insufficient
Solution Approach 1:
The patent implements a feedback mechanism where RSS measurements from multiple nodes are continuously processed to calculate crowd density estimates. The system compares current density estimates against threshold values and provides immediate feedback when dangerous density levels are detected, enabling timely intervention while maintaining privacy through RF-based detection.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring and processing RSS data to establish baseline crowd density patterns. Thresholds for dangerous density levels are pre-configured, allowing the system to quickly detect deviations from normal patterns and trigger alerts before unsafe situations develop, reducing detection response time.
3Reliability
If crowd density monitoring is implemented, then safety is improved, but the system complexity increases
Solution Approach 1:
The patent makes the wireless sensor network nodes multi-functional by having them simultaneously perform RF communication for data exchange and crowd detection through RSS measurements. This universality eliminates the need for separate dedicated detection devices, reducing overall system complexity while maintaining reliable safety monitoring capabilities.
Solution Approach 2:
The sensor network nodes perform self-service by using their own RF communication infrastructure to conduct crowd detection. The same transceivers used for data communication also measure signal attenuation caused by crowd density, eliminating the need for additional specialized detection hardware and simplifying the overall system architecture.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides accurate, privacy-friendly crowd monitoring, effective in indoor environments and under low-light conditions, enabling timely intervention in potentially dangerous crowd density changes.
Implementation Method 1
a wireless sensor network, WSN, throughout which radio-frequency, RF, signals are propagated
Implementation Method 2
Through received signal strength, RSS, measurements a crowd density is estimated within the area covered by the nodes
Data Source
AI summary
A method for estimating movements of a crowd between a first and second subregion in an area are monitored by a wireless sensor network. The wireless sensor network includes nodes configured to exchange a radio frequency signal through a first respective second link. The first respective second link crosses the first respective second subregion. The method includes the steps of exchanging radio frequency signals over the first and second link; and measuring respective first and second attenuations of the exchanged radio frequency signals over the first respective second link; and estimating based on a change in the attenuations a flow of the crowd between the first and second subregion. The estimating further includes estimating based on the first and second attenuations a density of the crowd in the first respective second subregion; and estimating based thereon a flux of the crowd between the first and second subregion.