Surveillance Camera Earthquake Detection via Video Vibration Analysis
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Solution Overview
Problem
Conventional earthquake detection apparatuses are expensive, difficult to universalize, and slow to transmit evacuation orders due to their fixed installation far from living areas, leading to increased economic and human damage from earthquakes.
Innovation Solution
An apparatus and method using pre-installed cameras to extract vibration signals from real-time surveillance videos, processing them through a deep magnification network and deep seismic classification network to detect earthquakes, allowing for early detection without the need for separate detection facilities and enabling quick evacuation orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional earthquake detection apparatuses are used, then detection reliability is improved, but cost increases and installation flexibility deteriorates
Solution Approach 1:
The patent reuses existing surveillance cameras for dual purposes: original security monitoring and earthquake detection. By extracting vibration signals from video footage captured by these cameras, the system eliminates the need for dedicated earthquake detection equipment, achieving universal utilization of infrastructure while reducing costs and improving installation flexibility
Solution Approach 2:
The system uses the camera's own recorded video data to detect earthquakes, without requiring separate sensors or additional hardware. The vibration information is extracted from the video frames themselves, allowing the existing camera infrastructure to serve both its original function and earthquake detection function simultaneously
2Measurement precision
If conventional earthquake detection apparatuses are installed at fixed observatories, then detection accuracy is improved, but response time deteriorates
Solution Approach 1:
The patent divides the earthquake detection network into multiple distributed camera units positioned throughout the living area. Each camera independently detects local vibrations, and the system aggregates data from multiple segments to achieve both high accuracy and rapid response. This segmentation allows parallel detection across multiple locations, reducing the time to detect and report earthquakes
Solution Approach 2:
The system transitions from traditional ground-based seismic sensors to aerial/video-based vibration detection. By capturing vibrations through camera footage rather than direct ground contact, the system creates a new detection dimension that enables both accurate measurement and rapid information transmission to evacuation centers
3Measurement precision
If deep magnification network is applied to magnify subtle motions, then earthquake detection sensitivity is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary processing by removing camera vibration noise before magnifying subtle earthquake motions. By pre-processing the video data to eliminate dominant vibration sources, the subsequent magnification operation can focus computational resources on enhancing only the subtle earthquake signals, reducing overall complexity while maintaining sensitivity
Solution Approach 2:
The deep magnification network applies different processing strengths to different frequency components of the vibration signal. Subtle earthquake motions in specific frequency ranges are magnified with higher computational effort, while other components receive standard processing. This localized approach to signal processing improves sensitivity to earthquake signals while managing computational complexity
Data Source
AI summary
An early earthquake detection method may comprise acquiring a frame image from a camera; acquiring a vibration signal from the frame image; removing a noise signal due to vibration of the camera from the vibration signal; acquiring a motion signal obtained by magnifying subtle motions from the noise signal-removed vibration signal; extracting vibration characteristics from the motion signal; estimating an occurrence of an earthquake by extracting a peak signal from the vibration characteristics; and determining whether an earthquake occurs by receiving earthquake estimation information from at least one other camera located within a certain range.