Crowd Density Estimation via Motion Line Analysis
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Solution Overview
Problem
Existing methods for estimating crowd density from video footage, especially in congested situations, face challenges in accuracy due to limited variation in feature quantities like corners and edges, and struggle to quantify the magnitude of horizontal swaying, leading to incomplete detection and increased costs with high-resolution cameras.
Innovation Solution
A crowd monitoring system that includes image and motion feature quantity analysis using a monocular camera, employing an image feature quantity acquiring unit, motion line acquiring unit, and a storage unit to store relations between feature quantities and density, allowing for accurate crowd density estimation through regression formulas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a monocular camera is used for crowd density measurement, then the device complexity is reduced, but the measurement precision deteriorates due to insufficient resolution for detecting horizontal swaying in high-density crowds
Solution Approach 1:
The patent transforms the measurement approach by changing from direct spatial resolution dependence to temporal motion pattern analysis. By extracting motion lines and calculating horizontal swaying magnitudes from video sequences, the system achieves accurate crowd density measurement using a monocular camera without requiring high spatial resolution.
2Measurement precision
If high-resolution cameras are used to detect horizontal swaying in high-density crowds, then the measurement precision is improved, but the device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical/optical solution (high-resolution camera) with a computational approach (motion analysis algorithm). By processing video sequences through motion line extraction and horizontal swaying magnitude calculation, the system achieves the same measurement precision without requiring expensive high-resolution hardware.
3Device complexity
If feature quantities such as corner number and edge quantity are used for crowd density estimation, then the device complexity is kept simple, but the measurement precision deteriorates in high-density situations where movement is restricted
Solution Approach 1:
The patent transitions from static image feature analysis to dynamic motion pattern analysis. By extracting motion lines from video sequences and calculating horizontal swaying magnitudes over time, the system captures dynamic crowd behavior that remains detectable even in high-density situations where static features become indistinguishable.
Data Source
AI summary
The present invention provides a crowd monitoring system with which it is possible to obtain a crowd density accurately, irrespective of the congestion state. This crowd monitoring system 100 is provided with: an image acquiring unit 101 which acquires a plurality of images; an arithmetic logic unit 108; and a storage unit 106 which stores information relating to relationships between image feature quantities and an object density, acquired in advance, and information relating to relationships between motion feature quantities and the object density. The arithmetic logic unit 108 comprises: an image feature quantity acquiring unit 103 which obtains image feature quantities of objects in the acquired images; a motion line acquiring unit 104 which obtains motion lines of the objects in the acquired images; a motion feature quantity acquiring unit 105 which obtains motion feature quantities of the objects on the basis of the obtained motion lines; and a crowd density acquiring unit 107. The arithmetic logic unit 108 is characterized in that it obtains a first estimated density of the objects on the basis of the obtained image feature quantities and the stored relationships between the image feature quantities and the object density, and obtains a second estimated density of the objects on the basis of the obtained motion feature quantities and the stored relationships between the motion feature quantities and the object density.