Spatio-Temporal Warning System Using Multi-Sensor Fusion
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Solution Overview
Problem
Current security and anti-crime surveillance systems face challenges in accurately detecting and responding to security and crime prevention situations due to limitations in sensor-based detection methods and the inefficiency of human-dependent monitoring systems, particularly in managing large amounts of video data and identifying specific events like border-crossing, theft, and assault.
Innovation Solution
A spatio-temporal situation data-based warning system that uses a sensor module to detect position, video, audio, vibration, motion, and environment information, combining this data with temporal and spatial data to determine if security events have occurred, and automatically transmitting warning signals to external systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a remote surveillance system using surveillance cameras is adopted, then security coverage can be expanded without increasing personnel, but the system becomes dependent on manual monitoring which reduces detection speed and increases response time
Solution Approach 1:
The patent replaces the mechanical human monitoring system with an automated sensor-based detection system. Multiple sensors (motion sensors, vibration sensors, audio sensors) automatically detect security events and trigger warnings, eliminating the need for human operators to continuously monitor cameras while maintaining expanded coverage area.
Solution Approach 2:
The surveillance system performs self-monitoring through automated sensor detection and automatic warning generation. The system detects security events independently and triggers warnings without human intervention, enabling the expanded coverage area to be monitored autonomously with reduced response time.
2Ease of operation
If sensor-based detection methods are used for personal security, then portability and ease of use are improved, but detection accuracy and reliability are insufficient
Solution Approach 1:
The patent combines multiple different sensors (motion sensors, vibration sensors, audio sensors) into an integrated detection system. This fusion of multiple sensing modalities compensates for the limitations of individual sensors, maintaining portability while significantly improving detection accuracy and reliability through multi-parameter analysis.
Solution Approach 2:
The detection system uses a composite approach by integrating multiple sensor types with different detection principles. This composite sensor system leverages the strengths of each sensor type (motion detection, vibration detection, audio detection) to achieve high reliability while maintaining the portability of personal devices.
3Measurement precision
If image surveillance systems are used to detect specific security events, then detection capability is improved, but the system becomes complex and expensive to implement
Solution Approach 1:
The patent extracts the essential detection functions from complex image surveillance systems and implements them using simpler, dedicated sensors. Instead of using sophisticated image analysis to detect motion, vibration, or audio events, the system uses specialized sensors optimized for each type of detection, reducing complexity and cost while maintaining or improving detection precision for specific security events.
Solution Approach 2:
The system replaces expensive, complex image surveillance infrastructure with cheaper, simpler sensor modules that can be easily deployed. The use of inexpensive motion sensors, vibration sensors, and audio sensors provides adequate detection capability for security events without the high cost and complexity of professional-grade image surveillance systems.
Data Source
AI summary
The warning system using spatiotemporal situation data according to the present invention comprises: a situation detection unit including a sensor module for sensing human-related data in environments requiring an early warning concerning security and anti-crime situations; a situation recognition unit which is configured to set a sensing region to be sensed by the situation detection unit and event conditions according to the spatiotemporal situation data, set a human subject as a human sensing condition according to the spatiotemporal situation data, and determine the occurrence of an event by comparing the human-related data sensed by the situation detection unit with data about the set sensing region, the event condition data according to the spatiotemporal situation data.