Multi-Sensor Security Event Detection With Context-Weighted Scoring
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Solution Overview
Problem
Conventional security systems are one-dimensional and prone to generating false positives, as they only monitor for a single data type, leading to unreliable detection of security events in environments like train stations.
Innovation Solution
A method and apparatus that collect sensor data from multiple sources, assign confidence scores to characteristics of security events, and generate a combined score using dynamic weights adjusted based on environmental states, to accurately determine the presence of security events and trigger alerts only when the score exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security systems monitor only a single data type, then the system complexity is low, but the detection reliability is poor and false positives increase
Solution Approach 1:
The patent combines multiple sensors of different types (audio, video, environmental) into a unified security monitoring system. The sensor fusion module integrates data from these diverse sources to create a comprehensive view of the environment, improving detection reliability by cross-validating signals across multiple modalities rather than relying on a single data type.
Solution Approach 2:
The security system is designed with multi-functional capabilities by incorporating sensors that detect various physical quantities (sound, light, temperature, motion). This universal monitoring approach allows the same system to detect multiple types of security events simultaneously, enhancing reliability without requiring separate specialized systems for each threat type.
2Measurement precision
If a security system uses multiple sensors and data types, then detection accuracy improves, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the security monitoring system into distinct functional modules: sensor data acquisition, preprocessing, feature extraction, event detection, and alert generation. Each module handles specific aspects of the multi-sensor data, breaking down the complex processing task into manageable stages that can be optimized independently, thereby reducing overall processing complexity while maintaining high detection accuracy.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates and normalizes data from different sensor types into a common format before further analysis. This intermediary module acts as a mediator that harmonizes diverse data streams, making them compatible for integrated processing without requiring complex custom handling for each sensor type, thus reducing processing complexity.
3Speed
If security systems generate alerts for every detected event, then response time is fast, but the number of false positives increases and officer workload increases
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors detected events and adjusts its detection thresholds and sensitivity based on historical data and false positive patterns. This feedback loop allows the system to learn from past performance, refining its alert generation criteria to maintain fast response times while reducing false positives by adapting to actual threat patterns in the environment.
Solution Approach 2:
The security system employs dynamic alert thresholds that adjust based on environmental context, time of day, and historical event patterns. Rather than using fixed thresholds that trigger alerts for all events, the system dynamically modifies its sensitivity, allowing faster response to high-risk events while filtering out low-risk false positives, thus balancing response speed with alert accuracy.
4Reliability
If manual monitoring is used by security officers, then the system is simple and low cost, but monitoring coverage is limited and reliability is poor
Solution Approach 1:
The patent enables the security system to perform self-monitoring and self-diagnosis functions through automated sensor data analysis and anomaly detection algorithms. The system automatically identifies security events, generates alerts, and even performs preliminary investigation without requiring constant human intervention, thereby improving monitoring reliability and coverage while reducing the operational burden on security officers.
Solution Approach 2:
The patent replaces manual mechanical monitoring by security officers with automated electronic sensor-based monitoring. The sensor network continuously scans the environment, automatically detecting and reporting security events without human physical presence, thereby significantly expanding monitoring coverage and improving reliability while reducing dependence on human operators.
Data Source
AI summary
Example implementations include aspects for controlling a security system monitoring an environment, comprising collecting sensor data from a plurality of sensors located in the environment, and determining whether a plurality of characteristics of a security event are present in the environment. A determination for each characteristic includes a confidence score indicative of a likelihood that the characteristic is present. The aspects further include identifying, from a plurality of pre-determined environment states, a state of the environment based on a current time and an event schedule of the environment, and generating a combined score that is a function of each respective confidence score and a plurality of weights, wherein each weight corresponds to a respective characteristic and has a value based on the state of the environment. The implementations further include determining that the combined score is greater than a threshold score, and generating an alert indicative of the security event.